6,296 research outputs found
Stochastic Query Covering for Fast Approximate Document Retrieval
We design algorithms that, given a collection of documents and a distribution over user queries, return a
small subset of the document collection in such a way that we can efficiently provide high-quality answers
to user queries using only the selected subset. This approach has applications when space is a constraint
or when the query-processing time increases significantly with the size of the collection. We study our
algorithms through the lens of stochastic analysis and prove that even though they use only a small fraction
of the entire collection, they can provide answers to most user queries, achieving a performance close to the
optimal. To complement our theoretical findings, we experimentally show the versatility of our approach
by considering two important cases in the context of Web search. In the first case, we favor the retrieval of
documents that are relevant to the query, whereas in the second case we aim for document diversification.
Both the theoretical and the experimental analysis provide strong evidence of the potential value of query
covering in diverse application scenarios
Graph compression using heuristic-based reordering
Inverted index has been extensively used in Information retrieval systems for document relatedqueries. We consider the generic case of graph storage using Inverted Index and compressing themusing this format. Graph compression by reordering has been done using traversal and clusteringbased techniques. In generic methods, the graph is reordered to arrive at new identifiers for thevertices. The reordered graph is then encoded using an encoding format. The reordering to achievemaximal compression is a well known NP complete problem, the Optimal Linear Arrangement.
Our work focuses on the inverted index format, where each node has its corresponding list ofneighbours. We propose a heuristic based graph reordering, using the property that the cost ofeach vertex is bound by its neighbour with largest vertex id. Consider, two vertices x and y withedges a and b respectively. If x\u3ey and a\u3eb, then cost of graph would come down, if the vertex idof x and y are interchanged. Further, experiments shows that using this heuristic helps in achievingcompression rates on par with distributed methods but with reduced utilization of computationresource
Agents, Bookmarks and Clicks: A topical model of Web traffic
Analysis of aggregate and individual Web traffic has shown that PageRank is a
poor model of how people navigate the Web. Using the empirical traffic patterns
generated by a thousand users, we characterize several properties of Web
traffic that cannot be reproduced by Markovian models. We examine both
aggregate statistics capturing collective behavior, such as page and link
traffic, and individual statistics, such as entropy and session size. No model
currently explains all of these empirical observations simultaneously. We show
that all of these traffic patterns can be explained by an agent-based model
that takes into account several realistic browsing behaviors. First, agents
maintain individual lists of bookmarks (a non-Markovian memory mechanism) that
are used as teleportation targets. Second, agents can retreat along visited
links, a branching mechanism that also allows us to reproduce behaviors such as
the use of a back button and tabbed browsing. Finally, agents are sustained by
visiting novel pages of topical interest, with adjacent pages being more
topically related to each other than distant ones. This modulates the
probability that an agent continues to browse or starts a new session, allowing
us to recreate heterogeneous session lengths. The resulting model is capable of
reproducing the collective and individual behaviors we observe in the empirical
data, reconciling the narrowly focused browsing patterns of individual users
with the extreme heterogeneity of aggregate traffic measurements. This result
allows us to identify a few salient features that are necessary and sufficient
to interpret the browsing patterns observed in our data. In addition to the
descriptive and explanatory power of such a model, our results may lead the way
to more sophisticated, realistic, and effective ranking and crawling
algorithms.Comment: 10 pages, 16 figures, 1 table - Long version of paper to appear in
Proceedings of the 21th ACM conference on Hypertext and Hypermedi
Query Expansion with Locally-Trained Word Embeddings
Continuous space word embeddings have received a great deal of attention in
the natural language processing and machine learning communities for their
ability to model term similarity and other relationships. We study the use of
term relatedness in the context of query expansion for ad hoc information
retrieval. We demonstrate that word embeddings such as word2vec and GloVe, when
trained globally, underperform corpus and query specific embeddings for
retrieval tasks. These results suggest that other tasks benefiting from global
embeddings may also benefit from local embeddings
Textual and content-based search in repositories of Web application models
Model-driven engineering relies on collections of models, which are the primary artifacts for software development. To enable knowledge sharing and reuse, models need to be managed within repositories, where they can be retrieved upon users’ queries. This article examines two different techniques for indexing and searching model repositories, with a focus on Web development projects encoded in a domain-specific language. Keyword-based and content-based search (also known as query-by-example) are contrasted with respect to the architecture of the system, the processing of models and queries, and the way in which metamodel knowledge can be exploited to improve search. A thorough experimental evaluation is conducted to examine what parameter configurations lead to better accuracy and to offer an insight in what queries are addressed best by each system.</jats:p
Reordering Rows for Better Compression: Beyond the Lexicographic Order
Sorting database tables before compressing them improves the compression
rate. Can we do better than the lexicographical order? For minimizing the
number of runs in a run-length encoding compression scheme, the best approaches
to row-ordering are derived from traveling salesman heuristics, although there
is a significant trade-off between running time and compression. A new
heuristic, Multiple Lists, which is a variant on Nearest Neighbor that trades
off compression for a major running-time speedup, is a good option for very
large tables. However, for some compression schemes, it is more important to
generate long runs rather than few runs. For this case, another novel
heuristic, Vortex, is promising. We find that we can improve run-length
encoding up to a factor of 3 whereas we can improve prefix coding by up to 80%:
these gains are on top of the gains due to lexicographically sorting the table.
We prove that the new row reordering is optimal (within 10%) at minimizing the
runs of identical values within columns, in a few cases.Comment: to appear in ACM TOD
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