You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines 
Chapter Title:  Parallel Coordinates and Starplots 
1. Abstract of Cited Paper (BIB001):  Our ability to accumulate large, complex (multivariate) data sets has far exceeded our ability to effectively process them in searching for patterns, anomalies and other interesting features. Conventional multivariate visualization techniques generally do not scale well with respect to the size of the data set. The focus of this paper is on the interactive visualization of large multivariate data sets based on a number of novel extensions to the parallel coordinates display technique. We develop a multi-resolution view of the data via hierarchical clustering, and use a variation of parallel coordinates to convey aggregation information for the resulting clusters. Users can then navigate the resulting structure until the desired focus region and level of detail is reached, using our suite of navigational and filtering tools. We describe the design and implementation of our hierarchical parallel coordinates system which is based on extending the XmdvTool system. Lastly, we show examples of the tools and techniques applied to large (hundreds of thousands of records) multivariate data sets. 
2. Abstract of Cited Paper (BIB002):  By means ofParallel Coordinates planar “graphs” of multivariate relations are obtained. Certain properties of the relationship correspond tothe geometrical properties of its graph. On the plane a point ←→ line duality with several interesting properties is induced. A new duality betweenbounded and unbounded convex sets and hstars (a generalization of hyperbolas) and between Convex Unions and Intersections is found. This motivates some efficient Convexity algorithms and other results inComputational Geometry. There is also a suprising “cusp” ←→ “inflection point” duality. The narrative ends with a preview of the corresponding results inR N . 
3. Abstract of Cited Paper (BIB003):  Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. As something relevant is detected in data, new questions arise, causing specific parts to be viewed in more detail. So EDA has a significant appeal: it involves hypothesis generation rather than mere hypothesis testing. The authors describe in detail and systemize approaches, techniques, and methods for exploring spatial and temporal data in particular. They start by developing a general view of data structures and characteristics and then build on top of this a general task typology, distinguishing between elementary and synoptic tasks. This typology is then applied to the description of existing approaches and technologies, resulting not just in recommendations for choosing methods but in a set of generic procedures for data exploration. Professionals practicing analysis will profit from tested solutions - illustrated in many examples - for reuse in the catalogue of techniques presented. Students and researchers will appreciate the detailed description and classification of exploration techniques, which are not limited to spatial data only. In addition, the general principles and approaches described will be useful for designers of new methods for EDA. 
4. Abstract of Cited Paper (BIB004):  A variety of user interfaces have been developed to support the querying of hierarchical multi-dimensional data in an OLAP setting such as pivot tables and more recently Polaris. They are used to regularly check portions of a dataset and to explore a new dataset for the first time. In this paper, we establish criteria for OLAP user interface capabilities to facilitate comparison. Two criteria are the number of displayed dimensions along which comparisons can be made and the number of dimensions that are viewable at once?visual comparison depth and width. We argue that interfaces with greater visual comparison depth support regular checking of known data by users that know roughly where to look, while interfaces with greater comparison width support exploration of new data by users that have no apriori starting point and need to scan all dimensions. Pivot tables and Polaris are examples of the former. The main contribution of this paper is to introduce a new scalable interface that uses parallel dimension axis which supports the latter, greater visual comparison width. We compare our approach to both recent table based and parallel coordinate based interfaces. We present an implementation of our interface SGViewer, user scenarios and provide an evaluation that supports the usability of our interface. 
5. Abstract of Cited Paper (BIB005):  Supporting visual analytics of multiple large-scale multidimensional data sets requires a high degree of interactivity and user control beyond the conventional challenges of visualizing such data sets. We present the DataMeadow, a visual canvas providing rich interaction for constructing visual queries using graphical set representations called DataRoses. A DataRose is essentially a starplot of selected columns in a data set displayed as multivariate visualizations with dynamic query sliders integrated into each axis. The purpose of the DataMeadow is to allow users to create advanced visual queries by iteratively selecting and filtering into the multidimensional data. Furthermore, the canvas provides a clear history of the analysis that can be annotated to facilitate dissemination of analytical results to stakeholders. A powerful direct manipulation interface allows for selection, filtering, and creation of sets, subsets, and data dependencies. We have evaluated our system using a qualitative expert review involving two visualization researchers. Results from this review are favorable for the new method.

Literature Review:
As opposed to the orthographic axes of scatterplots, parallel coordinates BIB003 stack the dimension axes of a multivariate data set in parallel and visualize items in the data set as polylines that connect the item's value for each dimension. Starplots  extend parallel coordinates by arranging the axes in a polar instead of a linear coordinate space, turning the open polylines representing data items into closed ones. The simplest approach to visualize aggregated parallel coordinates is to just use polylines as visual aggregates for the average of the aggregated data items. van Wijk and van Selow BIB001 describe the use of this representation for hierarchically clustered time-series data. However, the information conveyed through line aggregates is low (only the average value). Also, the line aggregates have no visual affordance that indicates that they are actually aggregates of underlying data items. To address this problem, Fua et al. BIB002 , and later Yang et al.  , describe a hierarchical visual aggregation approach for parallel coordinate diagrams based on bands that aggregate several polylines (visual data items) into a single band (visual aggregates) as wide as the extrema of the underlying data. This is an n-dimensional generalization of scatterplot boxes and hulls in two-dimensional space. Bands can also convey the average value using opacity just like scatterplot hulls and boxes. See Fig. 9 for examples of how to apply hierarchical aggregation to a standard parallel coordinate diagram. Both lines and bands can be trivially extended to starglyph diagrams BIB002 ,  (Fig. 10) . Recent work on starglyph diagrams introduced the notion of color histograms BIB006 ,  showing the data distribution on the surface of the opacity bands (Fig. 11) . This method could also be employed for standard parallel coordinate diagrams; Andrienko and Andrienko BIB004 show approaches to displaying quartiles and distributions in parallel coordinates using bands and beads, respectively. Along similar lines, the parallel sets  technique extends parallel coordinate displays with frequency-based visualizations of categorical data (Fig. 12) . Sifer BIB005 develops the idea further by removing the bands connecting data categories and relying merely on color for identification. However, these approaches do not maintain an aggregate hierarchy and are, thus, not true hierarchical visualization techniques.