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Maryland Politics
O'Malley wind farms face more challenges as Maryland legislature returns
By Aaron C. Davis
Aaron C. Davis
Investigative reporter
The day Maryland lawmakers left Annapolis nine months ago, Gov. Martin O'Malley chided them, saying the legislature had "choked" on his signature environmental initiative: a measure to subsidize development of a multibillion-dollar offshore wind farm.
The plan would have added a couple of extra dollars to every Marylander's monthly electric bill for 20 years and thousands onto those of the state's largest businesses. O'Malley (D) argued the costs would be worth it for about 2,000 jobs and a foothold for Maryland in a promising new green-energy market.
But as the legislature returns this week to Annapolis for the start to the 2012 session, there is little evidence that O'Malley's ambition for offshore wind has grown easier for lawmakers to swallow.
Rather, political currents that seemed poised to help propel the industry just a year ago have since turned against it. The high-profile collapse of Solyndra, the federally backed California solar company, and the paralyzing summer showdown over mounting federal debt have led Congress to cut off funding for loan guarantees and tax credits considered critical to the economics of the nascent offshore wind market.
"The situation has gotten worse — not better — for offshore wind since the last time it was up for debate," said Del. Dereck E. Davis, (D-Prince George's), chairman of one of two key committees that would have to back O'Malley's plan.
Members of the Maryland House of Delegates take the oath of office on the opening day of the Maryland General Assembly in 2011. (Mark Gail/WASHINGTON POST)
The result is a funding challenge that is even more vexing than the one O'Malley and offshore wind proponents faced a year ago and that underscores a problem common to many of the governor's most ambitious plans for the coming 90-day session.
O'Malley wants ratepayers to help cover the cost of making offshore wind energy competitive. He wants more gas tax revenue to increase road, bridge and transit construction. He wants environmental fees to bring in more money to reduce pollution flowing into the Chesapeake Bay. And he wants to accelerate state borrowing, despite shrinking property tax revenue to pay for it, to help spur improvements to public infrastructure.
After years of state budget cuts, each investment is important to Maryland's future, O'Malley says. But each would also hit state residents squarely in the pocketbook.
At the outset of a contentious election year, and with votes on legalizing same -sex marriage and other social issues sure to distract from economic ones, those conditions have made it impossible to predict what combination of higher taxes and fees — if any — will be palatable to Maryland's Democratic-controlled legislature.
"I definitely could not sign on to support offshore wind so long as ratepayers need to pay more," said Sen. Delores G. Kelley (D-Baltimore County).
Typically stalwart in support of O'Malley's causes, Kelley is among a large number of Democrats who have openly questioned the logic of moving forward with offshore wind while so many in the state are struggling to make ends meet.
"I just can't see why the citizens of Maryland should make a long-term commitment," Kelley said.
After last spring's failure, O'Malley was initially optimistic that he could build support for a second run at a subsidy for offshore wind. But as the landscape has shifted, he has grown more cautious. In a recent roundtable with reporters, O'Malley said only that he would do "something" about offshore wind when lawmakers return.
Behind the scenes, however, the governor's staff is finalizing a plan O'Malley aides say they hope will look far different, and be more acceptable, to state lawmakers.
Last year, O'Malley's legislation would have required utilities to purchase offshore wind power at a price far above the going rate for coal and gas so that offshore wind developers could guarantee a profit and Wall Street financing. The higher cost would have been passed on to ratepayers in the form of a line-item charge on monthly bills.
Under the new plan, modeled after one passed in New Jersey that has not been tested, Maryland would set up a kind of commodities market for offshore wind energy. The electricity created from the wind farm would be bought and sold in the form of renewable energy credits. The state's power generators need the credits to meet Maryland guidelines that require them to obtain a growing share of their power from renewable sources.
The new plan would increase ratepayers' bills, perhaps by an amount nearly equal to what last year's plan would have, but in a way that would obfuscate such costs, experts said.
The credits would not appear as a line-item charge but would be factored into the overall price of electricity sold in the state, raising base electric rates charged to Maryland customers.
If structured like the law in New Jersey, the plan could also make it easier for lawmakers to support by leaving key decisions about whether to move forward up to Maryland's Public Service Commission, which is controlled by O'Malley appointees.
The commission would decide whether the higher rates needed to subsidize offshore wind would be worth the jobs and other economic benefits derived from building the farm, probably 11 miles off the coast of Ocean City.
For O'Malley, following the New Jersey mold may prove to be a tough political pill. O'Malley has repeatedly criticized New Jersey Gov. Chris Christie (R) for his environmental record and called him an ineffective leader with failed policies.
Jim Lanard, a vocal industry proponent, however, said that after coming up short with Maryland Democrats, he was pleased O'Malley seemed poised to pursue the New Jersey model, which at least in that state has proved more appealing to some fiscal conservatives and the state's Republican governor.
"Governor Christie's perspective is this is about jobs and economic development; we would be happy to see Governor O'Malley follow suit," Lanard said.
State Senate Finance Chairman Thomas M. Middleton (D-Charles) said he could support a New Jersey-style offshore wind plan with some key changes.
He said that an independent body, not the PSC, should be charged with assessing the potential economic benefit of an offshore wind farm and that large manufacturing and other commercial customers should be exempt from rate increases.
"We're trying to create jobs, not interfere with them and add costs," Middleton said.
But it's unclear if even the New Jersey model will work without the federal loan guarantees and tax credits. The state is many months away from receiving its first proposals to weigh costs vs. economic rewards.
And last month, one of the nation's seemingly most ambitious wind developers, NRG Energy, said it was putting all of its offshore wind projects on hold — and walking away from a generous public subsidy to build a wind farm off the coast of Delaware — largely because the lack of federal loan guarantees and tax credits made the project "unfinanceable and financially untenable."
"It's just not an economically viable technology without billions and billions in subsidies," said Audra Parker, head of the Alliance to Protect Nantucket Sound, one of the groups leading what has been a decade-long effort to kill an offshore wind farm planned off the coast of Massachusetts.
"I think the appetite for these massive expenditures of federal dollars has dampened after Solyndra. People are seeing [offshore wind] as another money pit," Parker said.
In Maryland, a coalition of environmentalists, steelworkers and other proponents continues to promote a far different theory: The price of offshore wind will only become more competitive if the government can help spur development. And in the long run, that will make the upfront cost for offshore wind a better energy bet than the roller-coaster prices of fossil fuels.
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MIT's Wettest Test
Other requirements may come and go, but you still can't graduate without knowing how to swim.
Nicole Morell archive page
Each year, at the end of the summer, MIT's new students descend upon campus to get their feet wet—literally.
Students hit the pool at the Z Center for a 2014 swim test.
To meet one of MIT's General Institute Requirements before their first semester even starts, many first-year students attending orientation hop into the Zesiger Center pool for a swim test. To pass, they must swim 100 yards; there's no time limit. Most students do pass, some sign up for a swim course in place of the test, and some procrastinate as long as they can.
Though it has been an Institute requirement since the 1940s, the swim test, which students must complete to graduate, sneaks up on some seniors year after year.
"Two days before graduation in 1952, I received a note from the registrar's office that there was no record of my having passed the swimming certification. My diploma would be held until I passed it," remembers Dan Lufkin '52, SM '58. "I rushed over to the pool and jumped in. The instructor signed the proper form and I hot-footed it to the registrar. All was okay, and I graduated right on schedule."
"At MIT I tried to ignore the swimming requirement and at the start of my last semester, they informed me I still had to pass the swim test!" says Glenn Nelson '73. "Well, my housemate and I were in the same sinking boat and took the swimming class. Passed with flying colors and enjoyed it!"
Why does MIT have a swim test? Carrie Moore, director of physical education, says it has a purpose beyond worrying would-be graduates. "It is a self-survival skill. Research shows that most drownings occur in families where parents don't know how to swim," she explains. "Swimming also opens up several opportunities for students to take advantage of other water sports at MIT."
For the Institute's many international students, the test is especially relevant. "MIT has an international population that generally has not had access to swim courses. It's an important skill for students to acquire," Moore says.
Origins of the swim test remain somewhat murky, but Moore believes they are tied to MIT's long-standing physical education requirement.
"MIT had the physical education requirement from the beginning, so it just made sense to add swim sometime around World War II," she says.
MIT isn't alone in its swim requirement. Dartmouth, Cornell, Notre Dame, Columbia, Williams, Bryn Mawr, and Hamilton, among others, require students to pass a swim test to be eligible for graduation.
Many Class of 2018 students who elected to take the test at orientation weren't fazed by the requirement and seemed to appreciate it. "It was pretty easy. I think it's a kind of quirky tradition. I met some people here [at the pool], so it was nice to do it during my first year," says student Cynthia Fang.
"I'm happy they still have the test," says Hank Valcour '56. "It is just one of those things that is still there while the Institute has changed in so many ways."
Nicole Morell

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This butterfly figurine is made specially for art glass collection and for decorating of homes.
I received the order safe and sound, very good work!


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GRAPHIC: Elderly Home Depot employee dies after being pushed down by store robber, police say
December 05, 2022 at 4:55 pm EST
Robber pushes down elderly Home Depot employee who later died, police say
HILLSBOROUGH, N.C. — Police in North Carolina are searching for a thief who pushed down an elderly Home Depot employee who later died.
The incident happened on Oct. 18 at a Hillsborough, N.C. location of the Atlanta-based home improvement store.
*WARNING: Video may be upsetting to some.
Surveillance video shows a masked man leaving the store's garden area and pushing a shopping cart with three pressure washers, according to the Hillsborough Police Department.
Police identify 15-year-old girl shot to death at high school party in Clayton County
Ex-Georgia Amazon employees plead guilty to 'staggering' fraud to buy luxury cars, high end jewelry
Georgia State confirms student was killed in gas station shooting near campus
The video shows the employee, who hasn't been identified, attempt to stop the suspect, who simply pushes him over and keeps walking. The elderly employee fell to the ground, appearing to hit his head.
The employee died on Dec. 1 due to complications from his injuries. The North Carolina medical examiner has ruled the death a homicide.
The suspect left the scene in a white four-door Hyundai Sonata with an obscured North Carolina temporary tag. Police are asking for the public's help to identify the man.
Home Depot was founded in Marietta 44 years ago and the headquarters are in Cobb County.
The store has not released a statement on the employee's death.
Anyone with information is asked to contact Hillsborough police.
15-year-old shot, killed at high school party in Clayton County


Dale and Thomas printed canvas fabric popcorn bags are identical on both sides and have the Dale and Thomas logo displayed prominently. The logo measures approximately 6″ high x 6.25″ wide. The company motto is SHARE SOME. At the top of the bag is a .25″ red drawstring. These cheery red & white bags measure approximately 18″ by 24″.


Internet policy maverick Sunil Abraham feels that accessibility is key to democratising the Internet space.
The article by Nikhil Varma was published in the Hindu on September 2, 2014.
Forty-year-old Sunil Abraham essays many roles, of a social entrepreneur, free software advocate and as policy director of the Bangalore-based Centre for Internet and Society that focuses on accessibility, access to knowledge, internet governance, telecom and digital humanities.
When he finished his engineering degree and was job hunting in Bangalore, the internet was an entity he had heard about, but never worked on.
"Family constraints meant that I had to look for jobs that paid me more than the salaries offered by industries for a 9 to 6 job. I decided to knock doors in the non profit sector. I met T. Pradeep, who ran Samuha, an NGO in Northern Karnataka that focused on integrated rural development.


This past autumn I decided I wanted to do some designs that would work well on fabric. You can't get more autumnal than squirrels so I started by sketching a few. I had a lot of success with my Give Thanks fox and sunflower fabric designs a while ago but didn't want to repeat myself. So I spent a happy evening building myself a mood board inspired by something I love that is quintessentially autumnal - tan leather and heather grey knit. I'd also been quite taken by the grey pumpkins that kept popping up all over Pinterest. They contrast beautifully with the usual orange ones.
Once I had my palette established I created a collection of six designs featuring birds, squirrels and autumnal foliage. I think it came out nice and had a very different (less whimsical, more collegiate) feeling to the Give Thanks fabrics.


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Custom Rubber Ducks Make A Splash At 'The Best College Tradition'
Every year the same scene unfolds in the warm Florida sun: Thousands of college students in tank tops and bathing suits dance to music, splash in the water and generally act like overeager lunatics.
Does this familiar scene sound like spring break? It's actually a unique fall event that was awarded "Best College Tradition" in 2011 by the National Association for Campus Activities: the annual Spirit Splash at the University of Central Florida.
During each Homecoming Week since 1995, thousands of students line up for hours to receive their Spirit Splash shirt and congregate around the Orlando, FL-based school's Reflecting Pond. After a long countdown, students rush into the water for a chance at an even bigger prize: special themed rubber ducks that are tossed into the crowd.
"We want ducks," the students chant, with some even carrying fishing and butterfly nets to try to scoop up the rubber mascots.
A few years ago, supplier CelebriDucks custom designed the ducks for Spirit Splash to resemble the school mascot, the UCF Trojan. And they lived on long after the Spirit Splash – they were featured on TV, in bowl games and at other UCF events.
"Collegiate is one of our most popular lines," says Craig Wolfe, president of CelebriDucks. His company created the college duck line 15 years ago in response to a retailer's request. "The Florida Gators were our first prototype, and once the concept was approved, my sales guy sold 100,000 of the ducks in two months," he notes.
Since then, CelebriDucks has created school mascot ducks and other types – including one modeled after former Oregon Ducks football coach Mike Bellotti for a gameday giveaway.
CelebriDucks is working with a client that wants to take the ducks in a whole new direction, Wolfe says. The new line will be called Rubber Tubbers, and will feature a school mascot floating in an inner tube. "We are starting with The University of Southern California, and we hope to get it out in time for football season," says Wolfe.
"We expect this to be as big, or even bigger than the whole CelebriDucks line," he says. "We hope it will be the next bobblehead."
See full article with additional images at ASICentral.
Contact Ketterer for your next rubber duck campaign info@kettererco.com


Fayetteville State University will offer transfer students from Fayetteville Technical Community College a two-year pathway to a bachelor's degree, with a $10,000 price tag.
The $10K Pathway Plan will provide access to an affordable bachelor's degrees in accounting, birth to kindergarten (non-teaching), criminal justice, fire and emergency services administration, business administration (General), intelligence studies, nursing, professional studies, psychology, and sociology. While these degree programs are fully online, FSU will provide regular face-to-face contact with program participants when needed.
The FSU-FTCC partnership is one of several held between the university and proximate community and technical colleges.
This is awesome all HBCUs need to partner with the local community colleges in there area. Not only partner but put some funding behind it like this deal with FSU and FTCC.


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Kanye West and John Legend Seem to Be Having a Very Public Disagreement About Donald Trump
It started with some tweets, but now there are screenshots.
By Emma Baty
Yesterday, Kanye West went on a Twitter spree about politics, Donald Trump, and basically anything else that was on his mind at the time. It started with this tweet:
You don't have to agree with trump but the mob can't make me not love him. We are both dragon energy. He is my brother. I love everyone. I don't agree with everything anyone does. That's what makes us individuals. And we have the right to independent thought.
— ye (@kanyewest) April 25, 2018
Eventually, he tweeted a picture of a Make America Great Again hat that was signed by Trump, and Trump even tweeted back, and then Kim stepped in to ask him to clarify that he doesn't "agree with everything Trump does."
John Legend took notice of the interaction. He started tweeting, and although he didn't outrightly say he was talking about Kanye... he was basically talking about Kanye.
I imagine there's some comfort in imagining a future without racism and projecting that onto the present. Thinking if we just deny the truth, it doesn't exist. If history is erased, we don't have to deal with its consequences. However...
— John Legend (@johnlegend) April 25, 2018
Far too many people don't have the luxury of closing their eyes and ears. They feel it in their lives and can't act like what they see and know doesn't exist.
They know their family was destroyed by deportation or incarceration. They know their loved one was denied safe harbor because of their religion. They know their son or daughter was shot because their skin color evoked fear
They see the statistics about black applications for real estate or loans or employment being turned away because their name was Jamal and not James
I love that great, brilliant artists have the power to imagine a better future. But artists can't be blind to the truth.
The defining trait of Trump's campaign and political profile was his embrace of white supremacy. He made it clear every time he spoke. Some serious cognitive dissonance to ignore that for the other x% you might find appealing. I guess I agree with >1% of anyone's thoughts though
The last tweet directly calls out people who turn a blind eye to Trump's "white supremacist" tendencies, in this case, he's talking about Kanye and his history with Trump, and he says it would take "some serious cognitive dissonance to ignore" the bad things the President has done.
Right after the 2016 election, Kanye met with the then-president elect in Trump tower, and he faced serious backlash from his fans. At the time, Kanye addressed the issue on Twitter, saying "I wanted to meet with Trump today to discuss multicultural issues ... I feel it is important to have a direct line of communication with our future President if we truly want change."
If you were wondering where this leaves John and Kanye's friendship, today, Kanye tweeted a text exchange he had with John. John asked him to reconsider aligning himself with Trump, and he basically said John was trying to manipulate his free thought.
But he went on to post another one.
Guess that settles that!
Follow Emma on Instagram.
Emma Baty Emma Baty is the Associate Entertainment Editor at Cosmopolitan who focuses on movies and TV.
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Well. Donald Trump Tweeted at Kanye
Kanye West Literally Got Booed During SNL
Kanye Wests Meets With Donald Trump at White House
Kanye Deletes Trump Tweets


Designing and creating a dream wedding was a walk in the park for Lyn and Luke Hart. After all, nine months into their relationship the pair bought and renovated a 1924 bungalow. And so, on October 1, 2011, they tied the knot on a friend's fly-fishing ranch a few hours from their home in Portland, Oregon. Their DIY wedding, crafted and built from scratch by the bride and groom with help from friends and family, was made with love and highlighted the event's pristine natural surroundings.
The couple had spent the last two summers at Horseshoe Ranch, and after Luke proposed in December 2010, they knew it'd be ideal for their wedding. "We have seen Horseshoe Ranch in the summer sunshine, under two feet of snow, and covered with leaves -- it was beautiful every season," says Lyn. And so, the outdoor-loving pair knew their 100 guests would adore the striking Northwest countryside as well.
The suite, made by Kiwi Cards, incorporated a save-the-date postcard and an invitation packaged in a pocket envelope. The ranch's fly-fishing logo was incorporated, tying it all together.
Lyn and Luke took portraits with Ryan Wilcox of You Look Nice Today Photography. The wild landscape only enhanced the photos.
Enchanted Florist arranged local flowers in vibrant pink, coral, and yellow shades. Greenery, leaves, and stalks of wheat add a rustic touch.
The groom and his groomsmen wear Me and Matilda ties and Cole Haan shoes. Luke paired his Brooks Brothers suit with a Hamilton watch given to him by the bride.
Lyn accessorized her Tara Keely by Lazaro dress with a belt from Twigs and Honey and Boulet boots from Drew's Boots. Her diamond earrings were a wedding present from Luke, and a pearl bracelet belonging to a bridesmaid acts as her "something borrowed." The finishing touch? Her antique engagement ring from Antoinette Antique and Estate Jewelry.
The bridesmaids don BCBG dresses with Anthropologie earrings and carry bouquets of fresh lavender.
To showcase the groom's love of fly-fishing, a decorative lure from Royal Treatment Fly Fishing adorns his buttonhole.
Luke's nephew, Blake, serves as ring bearer. They gave him some Western wear in honor of the occasion.
The program, like the invitation suite, features the ranch's icon.
The bride walks down the aisle with her father, Tim, and mother, Janet, by her sides.
The couple wanted a backdrop that wouldn't detract from the area's natural beauty. Their handcrafted version, of rope-covered metal pipes and billowing strips of fabric, adds just the right amount of festive flair.
Readings of "Little Word, Little White Bird" by Carl Sandburg and "I Carry Your Heart With Me" by e.e. cummings made the service extra special.
The duo transformed Mason jars by spraying them with chalkboard paint and then writing each guest's name on them with chalk.
The father of the bride used old window frames, chicken wire, and mini clothespins to create unique seating-card displays.
The mother of the bride made jars of peach jam using fruit from Colorado, the family's home state, and strawberry jam from local berries the couple picked. "jam packed with love" tags, designed by Three Eggs Design, garnish each gift.
Barbecue chicken, ribs, and pulled pork, and sides of baked beans, potato salad, and salad were prepared by Wubba's BBQ Shack.
To illuminate the tented space, Lyn and Luke crafted hanging candelabras from one-foot-wide plywood boards, rope, and Mason jars. They filled each jar with an electric tea-light candle. The soft glow set the dinner and seating area apart from the dance floor and gave the tent a cozy, warm feel.
Simple and clean, white plates and silver cutlery rest on kraft paper place mats rubber-stamped with the ranch's emblem.
Old cedar-wood siding was purchased at a local rebuilding center and used to build three-foot-long "flower" box table arrangements.
A three-tiered lemon chiffon cake by Crave Bakery was topped with a garland made by the groom, who fashioned it out of Scrabble tiles, twine, a drill with a tiny bit, and sticks.
The couple take a spin on the checkered dance floor to George Strait's "River of Love." It fit both their love of country music and their wedding's riverside location.


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How internet giants deliver their data to the world
19. March 2019 7. November 2019 ~ René Schläfke
In the course of attending the lecture "Ultra Large Scale Systems" I was intrigued by the subject of traffic load balancing in ultra-large-scale systems. Out of this large topic I decided to look at traffic distribution at the frontend in detail and held a presentation about it as part of this lecture. As this subject has proven to be difficult to comprehend, as long as all related factors are considered, multiple questions remained open. In order to elaborate on these questions I decided to write this blog post to provide a more detailed view of this topic for those interested. Herein, I will not discuss the subject of traffic load balancing inside an internal infrastructure, however corresponding literature can be found at the end of this article. Despite concentrating only on the frontend part of the equation an in-depth look into the workings of traffic load balancing will be provided.
Kubernetes (K8S) everywhere, but how?
15. March 2019 26. March 2019 ~ Immanuel Haag
In the last months, nearly everybody has been talking about Kubernetes. It's incredible! This semester the Stuttgart Media University even held a training course on this topic. For DevOps or "cloud-computing specialist" mastering Kubernetes and the concepts around it is becoming more and more important.
Large Scale Deployment for Deep Learning Models with TensorFlow Serving
12. March 2019 12. March 2019 ~ Florian Wintel
"How do you turn a trained model into a product, that will bring value to your enterprise?"
In recent years, serving has become a hot topic in machine learning. With the ongoing success of deep neural networks, there is a growing demand for solutions that address the increasing complexity of inference at scale. This article will explore some of the challenges of serving machine learning models in production. After a brief overview of existing solutions, it will take a closer look at Google's TensorFlow-Serving system and investigate its capabilities. Note: Even though they may be closely related, this article will not deal with the training aspect of machine learning, only inference.
Inference and Serving
Before diving in, it is important to differentiate between the training and inference phases, because they have completely different requirements.
Training is extremely compute-intensive. The goal here is to maximize the number of compute operations in a given time. Latency is not of concern.
Inference costs only a fraction of the computing power that training does. However, it should be fast. When you query the model, you want the answer immediately. Inference must be optimized for latency and throughput.
There are two ways to deploy a model for inference. Which one to use largely depends on the use case. First, you can push the entire model to client devices and have them do inference there. Lots of ML features are already baked into our mobile devices this way. This works well for some applications e.g. for Face-ID or activity-detection on phones, but falls flat for many other, large-scale industrial applications. You probably won't have latency problems, but you are limited to the client's compute power and local information. On the other hand, you can serve the model yourself. This would be suitable for industrial-scale applications, such as recommender systems, fraud detection schemes, intelligent intrusion detection systems and so forth. Serving allows for much larger models, direct integration into your own systems and the direct control and insights that come with it.
Serving Machine Learning at Scale
Of course, it's never that easy. In most "real-world" scenarios, there isn't really such a thing as a "finished ML model". Consider the "Cross-industry standard process for data mining":
Fig. 1 – Back to the basics: the six phases of CRISP-DM. Source
It might be ancient, but it describes a key concept for successful data mining/machine learning: It is a continuous process [12]. Deployment is part of this process, which means: You will replace your productive models, and you will do it a lot! This will happen for a number of reasons:
Data freshness: The ML model is trained on historical data. This data can go stale quickly, because new patterns constantly appear in the real world. Model performance will deteriorate, and you must replace the model with one that was trained on more recent data, before performance drops too low.
Model revision: With time, retraining the model might just not be enough to keep the performance up. At this point you need to revise the model architecture itself, perhaps even start from scratch.
Experiments: Perhaps you want to try another approach to a problem. For that reason you want to load a temporary, new model, but not discontinue your current one.
Rollbacks: Something went wrong, and you need to revert to a previous version.
Version control and lifecycle management aren't exactly new ideas. However, here they come with a caveat: Since artificial neural networks are essentially "clunky, massive databases" [5], loading and unloading them can have an impact on performance. For reference, some of the most impactful deep models of recent years are a few hundreds of megabytes in size (AlexNet: 240MB, VGG-19: 574MB, ResNet-200: 519MB). But as model performance tends to scale with depth, model size can easily go to multiple gigabytes. That might not be much in terms of "Big Data", but it's still capable of causing ugly latency spikes when implemented poorly. Besides ML performance metrics, the primary concerns are latency and throughput. Thus, the serving solution should be able to [4]:
quickly replace a loaded model with another,
have multiple models loaded at the same time, in the same process,
cope with differences in model size and computational complexity,
avoid latency spikes when new models are loaded into RAM,
if possible, be optimized for GPUs and TPUs to accelerate inference,
scale out inference horizontally, depending on demand.
Serving Before "Model Servers"
Until some three years ago, you basically had to build your ML serving solution yourself. A popular approach was (and still is) using Flask or some other framework to serve requests against the model, some WSGI server to handle multiple requests at once and have it all behind some low-footprint web-server like Nginx.
Fig. 2 – An exemplary serving solution architecture. Source: [8]
However, while initially simple, these solutions are not meant to perform at "ultra large" scale on their own. They have difficulty benefiting from hardware acceleration and can become complex fast. If you needed scale, you had to create your own solution, like Facebook's "FBLearnerPredictor" or Uber's "Michelangelo". Within Google, initially simple solutions would often evolve into sophisticated, complex pieces of software, that scaled but couldn't be repurposed elsewhere [1].
The Rise of Model Servers
Recent years have seen the creation of various different model serving systems, "model servers", for general machine learning purposes. They take inspiration from the design principles of web application servers and interface through standard web APIs (REST/RPC), while hiding most of their complexity. Among simpler deployment and customization, model servers also offer machine learning-specific optimizations, e.g. support for Nvidia GPUs or Google TPUs. Most model servers have some degree of interoperability with other machine learning platforms, especially the more popular ones. That said, you may still restrict your options, depending on your choice of platform.
Fig. 3 – Exemplary model server for an image recognition task. Source: [10]
A selection of popular model serving and inference solutions includes:
TensorFlow Serving (Google)
TensorRT (Nvidia)
Model Server for Apache MXNet (Amazon)
MLflow
DeepDetect
Skymind Intelligence Layer for Deeplearning4j
TensorFlow-Serving
By far the most battle-tested model serving system out there is Google's own TensorFlow-Serving. It is used in Google's internal model hosting service TFS², as part of their TFX general purpose machine learning platform [2]. It drives services from the Google PlayStore's recommender system to Google's own, fully hosted "Cloud Machine Learning Engine". TensorFlow-Serving natively uses gRPC, but it also supports RESTful APIs. The software can be downloaded as a binary, Docker image or as a C++ library.
The core of TensorFlow-Serving is made up of four elements: Servables, Loaders, Sources and Managers. The central element in TensorFlow Serving is the servable [3]. This is where your ML model lives. Servables are objects, that TensorFlow-Serving uses for inference. For example, one servable could correspond to one version of your model. Servables can be simplistic or complicated, anything from lookup-tables to multi-gigabyte deep neural networks. The lifecycles of servables are managed by loaders, which are responsible for loading servables to RAM and unloading them again. Sources provide the file system, where saved models are stored. They also provide a list of the specific servables, that should be loaded and used in production, the aspired versions. Managers are the broadest class. Their job is to handle the full life cycle of servables, i.e. loading, serving and unloading the aspired versions. They try to fulfill the requests from sources with respect the specified version policy.
Fig. 4 – TensorFlow-Serving architecture overview. Source: [3]
When a servable is elevated to an aspired version, its source creates a loader object for it. This object only contains metadata at first, not the complete (potentially large) servable. The manager listens for calls from loaders, that inform it of new aspired versions. According to its version policy, the manager then executes the requested actions, such as loading the aspired version and unloading the previous one. Loading a servable can be temporarily blocked if resources are not available yet. Unloading a servable can be postponed while there are still active requests to it. Finally, clients interface with the TensorFlow-Serving core through the manager. Both requests and responses are JSON objects.
Simple Serving Example
Getting started with a minimal setup is as simple as pulling the tensorflow/serving Docker image and pointing it at the saved model file [16]. Here I'm using a version of ResNet v2, a deep CNN for image recognition, that has been pretrained on the ImageNet dataset. The image below is encoded in Base64 and sent to the manager as a JSON object.
Fig. 5 – Some random image to predict. Source
The prediction output of this model consists of the estimated probabilities for each of the 1000 classes in the ImageNet dataset, and the index of the most likely class.
Fig. 6 – Model output, class 771 corresponds to "running_shoe".
Implementing and hosting a multi-model serving solution for an industrial-scale web application with millions of users, just for benchmarks, is slightly out of scope for now. However, Google provides some numbers that should give an idea of what you can expect TensorFlow-Serving to do for you.
A strong point of TensorFlow-Serving is multi-tenancy, i.e. serving multiple models in the same process concurrently. The key problem with this is avoiding cross-model interference, i.e. one model's performance characteristics affecting those of another. This is especially challenging while models are being loaded to RAM. Google's solution is to provide a separate thread-pool for model-loading. They report that even under heavy load, while constantly switching between models, the 99th percentile inference request latency stayed in the range from ~75 to ~150 milliseconds in their own TFX benchmarks [2].
Google claims that the serving system on its own can handle around 100,000 requests per second per core on a 16 vCPU Intel Xeon E5 2.6 GHz machine [1]. That is however ignoring API overhead and model complexity, which may significantly impact throughput. To accelerate inference on large models with GPUs or TPUs, requests can be batched together and processed jointly. They do not disclose whether this affects request latency. Since late February (TensorFlow-Serving v1.13), TensorFlow-Serving can now work directly in conjunction with TensorRT [14], Nvidia's high-performance deep learning inference platform, which claims a 40x increase in throughput compared to CPU-only methods [15].
Usage and Adoption
In their paper on TFX (TensorFlow Extended), Google presents their own machine learning platform, that many of its services use [2]. TFX' serving component, TFS², uses TensorFlow-Serving. As of November 2017, TensorFlow-Serving is handling tens of millions of inferences per second for over 1100 of Google's own projects [13]. One of the first deployments of TFX is the recommender system for Google Play, which has millions of apps and over a billion active users (over two billion if you count devices). Furthermore, TensorFlow-Serving is also used by companies like IBM, SAP and Cloudera in their respective multi-purpose machine learning and database platforms [2].
Today's machine learning applications are very much capable of smashing all practical limits: DeepMind's AlphaGo required 1920 CPUs and 280 GPUs running concurrently in real-time, for inference, for a single "client" [6]. That example might be excessive, but the power of deep ML models does scale with their size and compute complexity. Deep learning models today can become so large that they don't fit on a single server node anymore (Google claims that they can already serve models up to a size of one terabyte in production, using a technique called model sharding [13]). Sometimes it is worth investing the extra compute power, sometimes you just need to squeeze that extra 0.1 percent accuracy out of your model, but often there are diminishing returns,. To wrap it up, there may be a trade-off between the power of your model versus latency, throughput and runtime cost.
When you serve ML models, your return on investment is largely determined by two factors: How easily you can scale out inference and how fast you can adapt your model to change. Model servers like TensorFlow-Serving address the lifecycle of machine learning models, without making the process disruptive in a productive environment. A good serving solution can reduce both runtime and implementation costs by a significant margin. While building a productive machine learning system at scale has to integrate a myriad different steps from data preparation to training, validation and testing, a scalable serving solution is the key to making it economically viable.
Olston, C., Fiedel, N., Gorovoy, K., Harmsen, J., Lao, L., Li, F., Rajashekhar, V., Ramesh, S., and Soyke, J. (2017). Tensorflow-serving: Flexible, high-performance ML serving.CoRR, abs/1712.06139
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The Renaissance of column stores
11. March 2019 20. March 2019 ~ Raimondo Lazzara
While attending the lecture 'Ultra Large Scale Systems' I got introduced into the quite intriguing topic of high-performance data storage systems. One subject which caught my special attention were column-oriented database management systems (column stores) about which I decided to give a presentation. Being quite lengthy and intricate, I realized that the presentation left my colleagues more baffled than informed. So I decided to write a blog post to recapitulate the topic for all those who were left with unanswered questions that day and for all the rest out there who might be interested in such matters. I believe this article, even though depicting a quite technical and specialized topic, is nevertheless of general interest because it shows how a system can be optimized for performance by emphasizing on inherent design characteristics.
So what are column stores and what do we need them for?
This may be the most eminent question that crosses the mind of people who hear the term 'column stores' for the first time. Well let me tell you what they aren't, a euphonic buzzword which, once uttered, will capture the attention of every IT geek in close vicinity. However, a rather matured technology that has been around since the early 70s and which has been going through constant architectonic refinements that allowed it to establish a foothold on the field of data storage systems used for large scale systems or big data management [1, 2]. Nevertheless, because of their quite specific area of application, column stores still cover a rather opaque field of technical innovation. This article, therefore, tries to provide a brief overview of the subject by giving insights into the architecture, design concepts and current technical advancements concerning column stores.
Most modern database management systems (DBMSs) rely on the N-ary Storage Model (NSM). Here records are contiguously stored starting from the beginning of each disk page while using an offset table at the end of the page to position the start of each tuple (record). Thus, within each page the tuples are stored in sequence until the maximum page length of the storage system has been reached and a new page has to be created (figure 1). Database systems centered on this model show good access times when executing queries that either insert or modify single tuples or that result in a projection of a limited number of complete tuples [3]. The major drawback of this model, however, is its poor cache performance because it often burdens the cache with unnecessary attributes [4]. In contrast, column stores follow an entirely different concept called Decomposition Storage Model (DSM) where tables are vertically fragmented storing each attribute in a separate column (figure 1). The different attribute values for each tuple can then be reassembled by correlating their absolute position within each page. Another approach is to use binary relations based on an artificial key (surrogate) that allows reconnecting the different attributes to generate a partial or complete reconstruction of the initial tuple [5]. The performance advantage of this model can be seen when executing queries that require operations on entire columns. Those include aggregation queries (where only subsets of the entire data are required) or scan operations. Furthermore, since the data composition of each column is very homogeneous with little entropy, much better compression ratios can be reached [6]. This becomes even more accentuated when increasingly larger datasets have to be processed.
Figure 1: Row-based vs. column-based storage models
Diving into the internals
Trying to determine whether to use a row-oriented or a column-oriented storage system will inevitably result in pondering about the pros and cons of the above-mentioned architectures. It is clear that both systems have their strengths and weaknesses and the choice, like so many times, entirely depends on the problem to be solved. This may be elaborated by taking a closer look into the subject using an example. Let's take the table depicted in figure 1 and imagine a row-oriented database system stored information about company employees in a similar manner. In that case, queries resulting in key lookups and extraction of single but complete records of employees would be executed with high performance by the system. This could be, for example, the search for a record of a specific employee by its 'ID' or 'Name'. This process could even be improved by putting indexes on high cardinality columns like the 'ID', for example, which would further speed up the search. Thus, an application or service operating on the database soliciting requests of that classification would definitely benefit from the advantages provided by a row-oriented storage system.
However, what about a request to determine the average age of all male employees stored in the database. This kind of analytical query could in the worst case result in a complete scan of the entire table and would generate a completely different strain on the system [7]. Even though it could be mitigated by the use of composite indexes which, however, are only feasible when the table contains a small number of columns. Latter on the other hand is not the case in many big data storage systems where rather hundreds of columns per table are the norm. Working with composite indexes here will sooner or later produce an immense processing overhead which in the long run would consume substantial system resources [8]. This ultimately means, that for systems containing tables with sizes in the range of several hundred gigabytes, many analytical queries could potentially initiate sequential scans of the entire dataset. For this scenario, column stores represent a better option because the query execution would, by design, be limited to only those attributes required for the final projection. This would spare computation resources by avoiding the necessity to scan large amounts of irrelevant data and, as a result, lead to overall better performance of the system. Consequently, the right choice of the database system should, therefore, be guided by the demands posed by the services operating on it, because they ultimately define the predominant query structure processed by the system.
Query processing models
Figure 2: Row-scanner implementation
To understand the subjects in the sections that follow, some principle design aspects on how row and column stores execute queries have to be elaborated. Thus, when comparing the implementations of query execution between the two systems, fundamental differences become obvious. Capitalizing on the previous example, let's observe the execution of the following simple SQL query "SELECT Name, Profession FROM Employees WHERE Age > 30". The expected result from the query would be a list of the names and professions of all employees being older than 30 years. The request leads to low-level database operations where scanning processes on the corresponding table will be performed to gather the necessary datasets. In the center of every query execution are scanners that apply predicates on tuples, generate projections and provide their parent operators with the corresponding output data.
In the case of the row-scanner implementation (figure 2), the execution process is quite straightforward. Here the data is fetched from the storage layer in the form of record batches on which the scanner will perform filter operations. The data will then be forwarded to the parent operator which aggregates the data to assemble the final projection [9]. Now in case of the column store, the process looks considerably different. As illustrated in figure 3, operations are executed based on single columns rather than entire tables. Here the initial scan operation is performed on a single column containing the data on which the predicate has to be applied. Hence, in the first step, values are filtered by submitting them to predicate evaluation, leaving only a subset of the original dataset. However, instead of returning the values directly, only a list of their corresponding column positions is returned. In the steps that follow, the positions are correlated with the columns containing the requested attributes to extract the corresponding values which are then aggregated to assemble the projection [9]. Thus, the example already indicates why analytical queries may perform better on column stores than on row stores. Instead of scanning the entire table to generate an output consisting of only a small subset of all attributes of the extracted records, operations are limited to that subset of attributes from the beginning, resulting in a significant reduction of the operational overhead.
Figure 3: Column-scanner implementation
Bound to be optimized
Simply storing data in the form of columns will not bring the improvements that can be expected from column stores. Actually, with few exceptions, they usually get outperformed by row stores in most scenarios. Consequently, a number of optimization techniques have been adopted over the past years yielding significant performance enhancements. This allowed column stores to be successfully utilized in areas where large datasets have to be handled like, for example, data warehousing, data mining or data analytics [4]. Therefore, the following section will give a brief overview of a selected number of optimization techniques which have been integrated into many modern column store systems today.
Given the characteristics of columnar data, using compression on such structures seems to be the most obvious approach to reduce disc space usage. Indeed, values from the same column tend to fall into the same domain and, therefore, display low information entropy and more value locality [10]. Those qualities allow compressing one column at a time while even permitting different compression algorithms for individual columns. In addition, if values are sorted within a column, which is common for column store systems, that column will become remarkably compressible [6]. Another technique is 'frequency partitioning' where a column is reorganized in such a way, that each page of the column shows as low information entropy as possible. To accomplish this, certain column stores reorganize columns based on the frequency of values that appear in the column and allow, for example, frequent values to be stored together on the same page [11]. The improvements of such methods are apparent and investigations suggest that while row stores allow average compression ratios of 1:3, column stores usually achieve ratios of 1:10 or better. Finally, in addition to lowering disk space usage, compression also helps to improve query performance. If data is compressed, then less time is spent in I/O operations during query execution because of reduced seek times, increased buffer hit rates and less transfer time of data from disk into memory and from there to the CPU [6, 10].
Dictionary Encoding
This form of compression works fine on data sets composed of a small number of very frequent values. For each value appearing in a column, an entry is created in a dictionary table. The values in the column are then represented by integer values referencing the positions in this table. Furthermore, dictionary encoding can not only be applied to single columns but also to entire blocks [12]. Another advantage of dictionary compression is that it allows working with columns of fixed length if the system keeps all codes of the same width. This can further maximize data processing speeds in systems that rely on vectorized query execution.
Figure 4: Examples of 'Dictionary' encoding and 'Run-Length' encoding
Run-Length Encoding
The encoding is well suited to compress columns containing repeating sequences of the same value by reducing them to a compact singular representation. Here, the column entries are replaced by triple values describing the original value, its initial start position and its frequency (run-length). Hence, when a column starts with 20 consecutive entries of the value 'male' than these entries can be reduced to the triple ('male', 1, 20). This compression form works especially well on sorted columns or columns with reasonable-sized runs constituted of the same value [10].
Figure 5: Examples of 'Bit-Vector' encoding, 'Differential' encoding and 'Frame of Reference' encoding
Bit-Vector Encoding
In this type of encoding for each unique value in a column, a bit-string with the same length as the column itself is generated. The string contains only binary entries designating a '1' if the value the string is associated with exists at the corresponding position in the column, or a '0' otherwise. 'Bit-Vector' encoding is frequently used when columns have a limited number of unique data values. In addition, there is also the possibility to further compress the bit-vector allowing to use the encoding even on columns containing a larger amount of unique values [10].
Differential Encoding and Frame of Reference Encoding
'Differential' encoding expresses values as bit-sized offsets from the previous value. A value sequence beginning with '10, 8, 6, 12′ for example, can be represented as '10, -2, -2, 6′. The bit-size for the offset value, however, is fixed and cannot be changed once established. Therefore, special escape codes have to be used to indicate values whose offset cannot be represented with the specified bit-size. The encoding performs well on columns containing sequences of increasing or decreasing values, thus, demonstrating value locality. Those can be inverted lists, timestamps, object IDs or sorted numeric columns. As a variation of the concept, there is also the 'Frame of Reference' encoding which works in a very similar way. The main difference here is that the offsets do not refer to the direct predecessor but to a reference value within the set. For example, the previous sequence '10, 8, 6, 12′ would be represented as '10, -2, -4, 2' with '10' being the reference value [13].
Operations on compressed data
Figure 6: Layout of a compression block
Performance gains through compression can be maximized when operators are able to directly act on compressed values without the need for prior decompression [14]. This can be achieved through the introduction of buffers that consist of column data in a compressed format providing an API which allows query operators to work directly on the compressed values. Consequently, a component wrapping an intermediate representation for compressed data termed a 'compression block' is added to the query executor (figure 6). The methods provided by the API can be utilized by query operators to directly access compressed data without having to decompress and iterate through it [6, 10]. The illustration in figure 7 should exemplify the design by showing how a query execution on compressed data would look like. The query is aimed to determine the number of male and female employees who work as accountants. In the first step, a filter operation is performed on the sorted and 'Run-length' encoded 'Profession' column by calling the corresponding API method which returns the index positions of those values passing the predicate condition (Profession = 'Accountant'). The positions can then be used to delimit the corresponding region within the 'Bit-Vector' encoded 'Gender' column. Finally, the number of males and females working as accountants can be calculated using a corresponding API method that sums up all occurrences of the value '1' within the interval. As seen, for none of the operations any decompression of the scanned data was necessary.
Figure 7: Example depicting query operations on compressed data
Vectorized execution
Figure 8: Tuple-at-a-time vs. vectorized execution
Most of the traditional implementation strategies for the query execution layer are based on the 'iterator' or 'tuple-at-a-time' model where individual tuples are moved from one operator to another through the query plan tree [10]. Each operator normally provides a next() method which outputs a tuple that can be used as input by a caller operator from further up the execution tree. The advantage of this approach is that the materialization of intermediate results is minimal. There is, however, another alternative called 'vectorized execution' where in contrast to the 'tuple-at-a-time' model, each operator returns a vector of N tuples instead of only a single tuple (figure 8). This approach offers several advantages [6]:
Reduction in interpretation overhead by limiting the amount of function calls through the query interpreters.
Improved cache locality by being able to adjust the vector size to the CPU cache.
Better profiling by allowing operators to execute all expression evaluation work in a vectorized fashion (i.e. array-at-a-time), keeping the overhead for measurements of individual operations low.
Taking advantage of the columnar format by reading larger data batches of N tuples at a time allowing array iteration with good loop pipelining techniques, so that operations can repeatedly be executed within one function call.
Early vs. late materialization
One fundamental problem when designing the execution plan of queries for column stores is to determine when the projection of columns should occur. In a column store, information of a logical entity or object is distributed over several column pages on the storage medium. As a result, during the execution of most queries, several attributes of a singular entity have to be accessed. In many cases, however, database outputs are expected to be entity-based and not column-based. Consequently, during every execution, the information scattered over multiple columns has to be reassembled at some point, to form 'rows' of information about the entity. This joining of tuples is a process very common for column stores and has been coined with the term 'materialization' [15]. In this context, there are principally two design concepts that address the problem of column projection called 'Early Materialization' and 'Late Materialization'. During query execution, most naive column stores first select the columns of relevance, construct tuples from their component attributes, and then execute standard row store operations on the resulting rows. This approach of constructing tuples early in the query plan called 'Early Materialization' will in many cases result in better performance for analytical queries when compared to those seen in typical row stores, however, much of the potential of column stores will still be left untouched.
Modern column stores adapted the concept of 'Late Materialization' where operations are performed on the basis of single columns as long as possible and projection of columns occurs late in the query plan. From this rises the necessity to work with intermediate 'position lists' to join operations that have been conducted on individual columns. This lists can be represented as a simple array of bit strings or as a set of ranges of positions. Those position representations can then be intersected to extract the values of interest which then confluent into the final projection [15]. Thus, the concept of 'Late Materialization' offers several advantages that result in significant performance boosts which can be attributed to the following characteristics:
Given specific selection and aggregation operations, it is possible to completely skip the materialization of some tuples.
Decompression of data for the reconstruction of tuples can be avoided which allows the continuous operation on compressed data in memory.
Cache performance can be improved while operating directly on column data because irrelevant attributes for given operations can be omitted.
Efficient CPU usage through operations on highly compressible position representations which, given their structure, are well suited for CPU processing.
Figure 9: Analytical query to be executed using late materialization
In the following, a typical query execution using late materialization as implemented in modern column store systems will be described. Here the query consists of a simple SQL-statement aimed to determine the number of all female employees over 30 years of age ordered by professions (figure 9). In this example, the intermediate lists are expressed in the form of bit-vectors representing the positions of those values that passed the predicates and on which bit-wise 'AND' operations can be executed.
The query execution illustrated in figure 10 is a select-project operation where essentially two columns of a table (Employees) are filtered, while subsequently a sum aggregation is performed to generate the projection. Thus, in the first step, the two predicates are applied to the 'Age' and 'Gender' columns which results in two bit-vectors representing only those values which passed the predicates. These are then intersected by applying a bit-wise 'AND' operation and the resulting bit-vector then used to extract the corresponding values from the 'Profession' column. In the last step, the results are aggregated to assemble the projection by grouping and summing the values from the previous operation.
Figure 10: Execution of query using late materialization
Virtual IDs
A possible way to structure columns within a column store is to associate individual columns with an identifier like, for example, a numeric primary key. Adding an identifier in such an explicit fashion, however, unavoidably introduces redundancy and increases the amount of data to be stored on the disk. To solve this problem, modern database systems try to avoid additional columns containing solely IDs by substituting them with virtual identifiers which represent the position (offset) of the tuple in the column [5]. The design can be further enhanced by implementing columns composed of fixed-width dense arrays. This allows storing attributes of an individual record at the same position across all the columns of a table. In combination with offsets, the design permits to significantly improve the localization of individual records. A value at the i-th position of a table EMP, for example, could be located and accessed by just calculating 'startOf(EMP)+i*width(EMP)'.
Database cracking
Sorted columns are a helpful measure to significantly improve the performance of column stores, including the realization of high compression ratios. However, common approaches require a complete sorting of columns in advance, demanding idle time and workload knowledge. More dynamic approaches have brought forward architectures aiming to perform such tasks incrementally by combining them with query execution. The principal motivation is to continuously change the physical data store with every executed query. Sorting is consequently done adaptively in a continuous manner and limited to the accessed sections of a column [16]. Therefore, each query performs a partial reorganization of all processed columns making subsequential access faster. This process is called 'Database cracking' which allows using the database system immediately once data is available. It is an interesting approach to adaptive indexing because on every range-selection query, the data is reorganized and compartmentalized using the provided predicates as pivots. In that manner, the optimal performance is achieved incrementally without the prior need to analyze the expected workload, tune the system and create indexes. Thus, figure 11 shows an example of a search request where two consecutive queries search and 'crack' a column ('Age'). The first query subdivides the column into three pieces while the second query further improves the partitioning process. The final result is a column that is partially sorted and comprised of five value ranges. Consequently, the structure of the column data represents a reflection of the query structure and thus constitutes an adaption to the data requirements of the applications (services) accessing the database.
Figure 11: Adaptive indexing of column using database cracking
The column store concept, albeit nearly half a century old, has received considerable attention during the last decade. This is due to the fact that column stores exceed when it comes to performing analytical-style processing of large datasets [9]. This has made them the storage system of choice especially for applications operating with OLAP-like workloads which rely on very complex queries often involving complete datasets. Investigations have shown, however, that substantial improvements of the basic concept of column stores are necessary to truly reap the benefits such an architecture may provide. Therefore, several optimizations have been introduced over the past years, some of which have been discussed here. Those include compression, API-based compression buffers, vectorized processing, late materialization, virtual IDs and database cracking. All of which aim to significantly improve the processing time by tackling performance issues from different angles. When adding such optimization techniques column stores outperform row stores by an order of magnitude on analytical workloads [8]. This also indicates that the architectural design will be of interest even in the years to come because of the ever-growing number of large-scale, data-intensive applications with high workload. Those include scientific data management, business intelligence, data warehousing, data mining, and decision support systems.
Finally, there are also still directions for future developments that are worth investigating like, for example, hybrid systems that are partially column-oriented. Those could be realized in the form of architectures that store columns grouped by access frequency or that adapt to access patterns allowing to switch between column-oriented and row-oriented table structures when needed. Another issue to be addressed are the loading times of column stores, which still do not perform well when compared with row stores, especially if there are many views to materialize. Thus, studies on possible new algorithms that could alleviate the problem by substantially improve read performance would surely be an interesting field for future investigations.
[1] S. Melnik, A. Gubarev, J. J. Long, G. Romer, S. Shivakumar, M. Tolton and T. Vassilakis, Dremel: Interactive Analysis of Web-scale Datasets, Proceedings of the VLDB Endowment, VLDB Endowment, 2010, Vol. 3(1-2), pp. 330-339.
[2] D. J. Abadi, P. A. Boncz and S. Harizopoulos, Column-oriented database systems, Proceedings of the VLDB Endowment, VLDB Endowment, 2009, Vol. 2(2), pp. 1664-1665.
[3] D. Bößwetter, Spaltenorientierte Datenbanken, Informatik-Spektrum, Springer, 2010, Vol. 33(1), pp. 61-65.
[4] A. El-Helw, K. A. Ross, B. Bhattacharjee, C. A. Lang and G. A. Mihaila, Column-oriented Query Processing for Row Stores, Proceedings of the ACM 14th International Workshop on Data Warehousing and OLAP, ACM, 2011, pp. 67-74.
[5] S. Idreos, F. Groffen, N. Nes, S. Manegold, K. S. Mullender and M. L. Kersten, MonetDB: Two Decades of Research in Column-oriented Database, IEEE Data Engineering Bulletin, 2012, Vol. 35(1), pp. 40-45.
[6] D. J. Abadi, Query execution in column-oriented database systems, Massachusetts Institute of Technology, 2008.
[7] D. J. Abadi, Column Stores for Wide and Sparse Data, CIDR, 2007, pp. 292-297.
[8] D. J. Abadi, S. R. Madden and N. Hachem, Column-stores vs. row-stores: how different are they really?, Proceedings of the 2008 ACM SIGMOD international conference on Management of data, 2008, pp. 967-980.
[9] S. Harizopoulos, V. Liang, D. J. Abadi and S. Madden, Performance tradeoffs in read-optimized databases, Proceedings of the 32nd international conference on very large data bases, 2006, pp. 487-498.
[10] D. Abadi, S. Madden and M. Ferreira, Integrating compression and execution in column-oriented database systems, Proceedings of the 2006 ACM SIGMOD international conference on management of data, 2006, pp. 671-682.
[11] V. Raman, G. Swart, L. Qiao, F. Reiss, V. Dialani, D. Kossmann, I. Narang and R. Sidle, Constant-Time Query Processing, IEEE 24th International Conference on Data Engineering (ICDE '08), IEEE Computer Society, 2008, pp. 60-69.
[12] P. Raichand, A short survey of data compression techniques for column oriented databases, Journal of Global Research in Computer Science, 2013, Vol. 4(7), pp. 43-46.
[13] J. Goldstein, R. Ramakrishnan and U. Shaft, Compressing relations and indexes, Proceedings 14th International Conference on Data Engineering, IEEE, 1998, pp. 370-379.
[14] O. Polychroniou and K. A. Ross, Efficient lightweight compression alongside fast scans, Proceedings of the 11th International Workshop on Data Management on New Hardware, 2015, pp. 9.
[15] D. J. Abadi, D. S. Myers, D. J. DeWitt and S. R. Madden, Materialization strategies in a column-oriented DBMS, IEEE 23rd International Conference on Data Engineering, 2007, pp. 466-475.
[16] S. Idreos, M. L. Kersten and S. Manegold, Self-organizing Tuple Reconstruction in Column-stores, Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, ACM, 2009, pp. 297-308.


Satiate your appetite for local delights at The Food Gallery! Established as a dining concept that showcases Singapore's cultural diversity and uniqueness through a vast array of cuisines, you can find all-time favourites like Hainanese Steamed Chicken Rice, Cantonese Wanton Noodles, Indian Roti Prata, Malay Satay and Mee Rebus, and healthy Vegetarian Hotpot amongst so many other delicious dishes. Here you will also find the iconic Kedai Killiney Kopitiam where you can enjoy traditional thick and fluffy Kaya Toast! Pair it with an aromatic cup of freshly brewed coffee and tea and you are all set to immerse yourself in a truly Singaporean experience indeed.


YOU DECIDE is an Italian fashion company manufacturing fast fashion clothing for women and girls. Low minimums at a great value for money.
All products are made in Italy. YOU DECIDE clothes are manufactured from quality fabrics and accessories. Dresses, tops, blouses, blue jeans, sweaters, jackets, T-shirts, pants, and skirts are designed along the latest fashion trends.
Overside sleeveless jumpsuit with princes seam pockets.
YOU DECIDE is an Italian brand dealing with fast fashion clothing women's wear. It offers quality products with remarkable style content, thanks to great care dedicated to production processes and the choice of materials, matching all of this with affordable prices. Furthermore, our products are made by qualified Italian personnel who are mastering the best experience in the sector.
YOU DECIDE typically sells its pronto moda clothing to wholesalers, but also serves retailers in various countries and regions. Specialty shops and boutiques are very enthusiastic customers, because of affordable minimand really affordable wholesale prices for our dresses and clothes. YOU DECIDE products are also great for online shops.


The Ashby is a +/- 3,200 square foot, 4 bedroom, 2-1/2 bath plan, with an option for a 3rd bath. Large bedrooms, walk-in closets and all tile showers are standard in this home. Open concept space on the main level makes entertaining and family time easy!


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Kylie Jenner has been teasing #SummerSurprises for her Kylie Cosmetics line all season long. We got three new mattes, the first-ever eyeshadow palette and now, the limited edition Birthday Edition collection.
In case you missed it, the almost 19-year-old spent the weekend teasing a little black pouch on social media.
We finally have the answer. Yesterday, the Lip Kit queen revealed the contents of the mysterious bag on Snapchat (duh). There's nine, yes, nine, new products released in honor of King Kylie's birthday on August 10th.
First up, there's the makeup bag with the Kylie Cosmetics lip-dripping logo.
Next, Kyliner. The debut eyeliner kit comes in Dark Bronze with a pencil and gel liner version and a brush to apply it.
Next up, is a set of six mini matte lipsticks including, Dolce K, Exposed, Candy K, Koko K, Kristen and a new birthday shade named after Kylie's zodiac sign, Leo.
There's more. The Lip Kit reveals continue with Poppin, a shimmery gold gloss, and Lord, a metal matte lipstick.
BTW, if you notice a gold theme here, that's because all the products are infused with THE REAL STUFF. Gold, that is. King Kylie or King Midas?
OK, next is créme KYSHADOW!!! There's two colors, Gold (which is more like a copper) and Rose Gold.
Also, if you're following along with all these videos, then you know Kylie is pulling all of these products out of the new cosmetic bag, which must have been inspired by Hermoine's bag with the Undetectable Extension Charm. This thing fits a lot of stuff!!!
The entire Birthday Edition collection goes on sale Monday, August 1st at 3 PM PST. It will supposedly be available until Kylie turns the big 1-9 on Wednesday, August 10th, but we have a strong, strong feeling everything is going to go real quick. So make sure to have your browser open and credit card ready to go today if you want to snag something (anything!!!!)!


A miracle is the hand of God working in the supernatural to effect a change in the natural beyond human and scientific explanation.
Praise is celebrating the faithfulness of God and not the happenings around us.
Praising God makes you a wonder to your world.
High praise is a medium through which you provoke the help of God to level up every barriers – Joshua 6:20. Miracle is God's divine intervention in the affairs of man.
When you praise God, you celebrate the efficacy of scriptures.
1. To turn insufficiency to sufficiency.
2. For nothing to die around you.
3. For supernatural breakthroughs – Psalm 67:6. Supernatural breakthroughs have no human limitation.
For praise to produce, Praise God wholeheartedly in faith and with an expectation.


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Alliancebernstein L.P. now owns 5,571,441 shares of the railroad operator's stock worth $678,044,000 after acquiring an additional 192,984 shares during the period. Edwards Life now has $23.24 billion valuation.
In other news, Chairman James A. Squires sold 24,407 shares of the firm's stock in a transaction on Tuesday, August 15th. Another trade for 8,500 shares valued at $61,415 was bought by Johnson Lewis M. It is down 0.00% since October 4, 2016 and is. It has underperformed by 1.29% the S&P500. Union Pacific Corporation (UNP) has an EBITDA margin of 38.55%, compared to an EBITDA margin of 45.79% for Norfolk Southern Corporation (NSC). (NYSE:NSC) or 57,837 shares. (NYSE:NSC) on Friday, May 5.
The average 1-year price target for Norfolk Southern Corporation (NSC) - averaging the work of different analysts - reveals a mean PT of $125.16/share.
Norfolk Souther Corporation announced that its Board of Directors has approved a stock buyback plan on Tuesday, September 26th that permits the company to buyback 50,000,000 outstanding shares. About 532,495 shares traded. For Norfolk Southern Corporation (NYSE:NSC), Stochastic %D value stayed at 48.33% for the last 9 days. It has outperformed by 17.62% the S&P500. Analysts anticipate that Norfolk Souther Corporation will post $6.33 earnings per share for the current year.
The average investment recommendation on a scale of 1 to 5 (1 being a strong buy, 3 a hold, and 5 a sell) is 2.30 for UNP and 2.60 for NSC, which implies that analysts are more bullish on the outlook for NSC. Therefore 20% are positive. Norfolk Southern Corp. has a consensus rating of "Hold" and an average price target of $105.78. (NYSE:NSC) for 568 shares. Analysts use a stock's beta, which measures the volatility of a stock compared to the overall market, to measure systematic risk.
TRADEMARK VIOLATION WARNING: "Norfolk Souther Corporation (NSC) Holdings Lowered by ProShare Advisors LLC" was originally posted by Week Herald and is the property of of Week Herald. It is positive, as 43 investors sold NSC shares while 347 reduced holdings. The stock has "Equal-Weight" rating by Barclays Capital on Monday, April 11. Cowen & Co has "Outperform" rating and $89 target. Pennsylvania-based First Natl Tru has invested 0.2% in Norfolk Southern Corp. The firm has "Hold" rating given on Tuesday, August 11 by Topeka Capital Markets. The firm has "Underperform" rating by Avondale given on Wednesday, October 12.
Since May 4, 2017, it had 0 buys, and 5 insider sales for $515,842 activity. The insider Hurlbut Thomas E sold $166,839.
More recent Norfolk Southern Corp.
Shares are trading at $130.50 which is just over $124.72, the stock's 50 day moving average and marginally over the 200 day moving average of $119.20. (NYSE:NSC) on Friday, May 5. LOCKHART MICHAEL D also sold $117 worth of Norfolk Southern Corp. The Company is involved in the rail transportation of raw materials, intermediate products and finished goods primarily in the Southeast, East and Midwest, and through interchange with rail carriers, to and from the rest of the United States. As of December 31, 2016, the Company operated approximately 19,500 miles of road primarily in the East and Midwest. NSC's profit will be $466.90M for 20.23 P/E if the $1.62 EPS becomes a reality.
Investors sentiment increased to 0.87 in 2017 Q2. Its down 0.92, from 1.75 in 2017Q1. Norfolk Souther Corporation's payout ratio is now 39.61%.
These funds have shifted positions in (NSC). Comparatively, NSC's free cash flow per share was +0.37. Its up 201.47% from 6,800 shares previously. (NYSE:NSC). Bb&T Secs Ltd invested in 114,295 shares or 0.2% of the stock. 10,981 were accumulated by Leavell Investment. National Bank & Trust Of America Corporation De has 2,200 shares.
For the current quarter, 25 analysts are projecting a mean EPS of $1.61/share. Brown Brothers Harriman holds 14,313 shares or 0.01% of its portfolio. Pillar Pacific Capital Management LLC lifted its stake in shares of Norfolk Souther Corporation by 1.2% in the 2nd quarter. (NYSE:NSC). Renaissance Limited holds 499,600 shares or 0.08% of its portfolio. Gw Henssler And Assoc Ltd invested 0.26% of its portfolio in Norfolk Southern Corp. On Oct. 4, 2017 NSC saw a significant decline, closing the day at $131.57, or a decline of $1.07 or (0.81%). The fund owned 20,251,999 shares of the railroad operator's stock after acquiring an additional 700,567 shares during the quarter. Wagner Bowman Mgmt invested 0.34% of its portfolio in Norfolk Southern Corp. Northern Corp reported 25,935 shares stake. Norfolk Southern Corp. had 68 analyst reports since July 28, 2015 according to SRatingsIntel. Jefferies maintained the shares of EW in report on Friday, May 26 with "Buy" rating. The rating was maintained by RBC Capital Markets with "Sell" on Thursday, July 6. Firm's net income measured an average growth rate of 4.50%. Morgan Stanley reaffirmed an underweight rating and set a $75.00 price objective (down from $77.00) on shares of Norfolk Souther Corporation in a research report on Monday. The firm has "Neutral" rating by Buckingham Research given on Wednesday, January 18.
One of the key things investors look for in a company is the ability to grow earnings at a high compound rate over time.
It says that operators with lower LTE availability tend to have lower overall speeds since customers are also on 3G speeds. Idea and Airtel gave national 4G-latency victor Vodafone a stiff challenge in our regional ping tests.
Danley , de 62 años, estaba en Filipinas cuando ocurrió la tragedia que dejó más de 55 personas muertas y sobre 400 heridos . La Casa Blanca ha anunciado que el presidente Donald Trump hará una declaración a lo largo de la mañana sobre lo ocurrido.
And they don't know how to negotiate between all the competing interests in their own party, never mind in the country as a whole. In any case, for some working families, that sum would simply mean having to rob Peter a little bit less each month to pay Paul.
He spoke of the method he would defeat McGregor, claiming that he didn't need to hit him that hard to knock him out. He continued: "It is, that's why I told him to his face to bring his balls".
Mugabe said she had intervened on behalf of her sons who were "in trouble with a drunken young woman". Mugabe, however, disputed this, and claimed that Engels had threated her with a knife.
Catalan leader Carles Puigdemont will speak in the regional parliament next Tuesday, a parliamentary spokeswoman said on Friday. Puigdemont and other Catalan leaders have said they are not afraid of going to jail over their independence bid.
Georgia , Florida State and Auburn were the finalists for his services, but the Bulldogs had some built-in advantages. Eason and Fields are consensus five-star talents, and Fromm's a high four-star whom some rated as a five-star.
Finally, Financial Architects Inc lifted its position in shares of Wynn Resorts, Limited by 4.2% during the 2nd quarter. Louisiana State Employees Retirement Sys has 0.03% invested in Wynn Resorts, Limited (NASDAQ: WYNN ) for 5,000 shares.
The transaction was disclosed in a filing with the Securities & Exchange Commission, which is accessible through this link . The company has experienced volume of 2,895,386 shares while on average the company has a capacity of trading 1.97M share.
"A Mi-17 V5 helicopter was on an Air Maintenance Mission, crashed at 6 a.m. [00:30 GMT] in Arunachal Pradesh ". At least five people - all of them Air Force personnel - are feared to have been killed in the crash.


text:
Q: Remove domain user access to ubuntu machine I have configured my Ubuntu server to allow login using Windows AD credentials.
I have added enabled a domain group to login to ubuntu machine(by adding them in sudoer file)
#%mydomain\\domain^users ALL=(ALL) ALL
Now I want to remove the login access of these group members to the machine.
I tried modifying sudoer(removed #%mydomain\\domain^users ALL=(ALL) ALL )
But still the users of that group  is able to login.
How can I remove the access.

A: To enable access to one group only to ubuntu run the below command
sudo lwconfig RequireMembershipOf "Domain\testusers"
the above command only allows testusers group to login to the machine.
In my case I am using likewise-open to enable admin access

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