You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We consider the problem of similarity search within a set of top-k lists under the Kendall's Tau distance function. This distance describes how related two rankings are in terms of concordantly and discordantly ordered items. As top-k lists are usually very short compared to the global domain of possible items to be ranked, creating an inverted index to look up overlapping lists is possible but does not capture tight enough the similarity measure. In this work, we investigate locality sensitive hashing schemes for the Kendall's Tau distance and evaluate the proposed methods using two real-world datasets.

Cited Abstract(s):
[1]: Abstract.The efficient retrieval of data items on set-valued attributes is an important research topic that has attracted little attention so far. We studied and modified four index structures (sequential signature files, signature trees, extendible signature hashing, and inverted files) for a fast retrieval of sets with low cardinality. We compared the index structures by implementing them and subjecting them to extensive experiments, investigating the influence of query set size, database size, domain size, and data distribution (synthetic and real). The results of the experiments clearly indicate that inverted files exhibit the best overall behavior of all tested index structures.

[2]: Motivated by the urgent need to improve the efficiency of similarity queries, approximate similarity retrieval is investigated in the environment of a metric tree index called the M-tree. Three different approximation techniques are proposed, which show how to forsake query precision for improved performance. Measures are defined that can quantify the improvements in performance efficiency and the quality of approximations. The proposed approximation techniques are then tested on various synthetic and real-life files. The evidence obtained from the experiments confirms our hypothesis that a high-quality approximated similarity search can be performed at a much lower cost than that needed to obtain the exact results. The proposed approximation techniques are scalable and appear to be independent of the metric used. Extensions of these techniques to the environments of other similarity search indexes are also discussed.

[3]: We consider the problem of indexing high-dimensional data for answering (approximate) similarity-search queries. Similarity indexes prove to be important in a wide variety of settings: Web search engines desire fast, parallel, main-memory-based indexes for similarity search on text data; database systems desire disk-based similarity indexes for high-dimensional data, including text and images; peer-to-peer systems desire distributed similarity indexes with low communication cost. We propose an indexing scheme called LSH Forest which is applicable in all the above contexts. Our index uses the well-known technique of locality-sensitive hashing (LSH), but improves upon previous designs by (a) eliminating the different data-dependent parameters for which LSH must be constantly hand-tuned, and (b) improving on LSH's performance guarantees for skewed data distributions while retaining the same storage and query overhead. We show how to construct this index in main memory, on disk, in parallel systems, and in peer-to-peer systems. We evaluate the design with experiments on multiple text corpora and demonstrate both the self-tuning nature and the superior performance of LSH Forest.
Helmer and Moerkotte [1] present a study on indexing set-valued attributes as they appear for instance in object-oriented data -bases. Retrieval is done based on the query's items; the result is a set of candidate rankings, for which the distance function can be computed. For metric spaces, data-agnostic structures for indexing objects are known, like the M-tree by [3] [2] ; but Kendall's Tau over incomplete list is not a metric.