664 research outputs found
On sampling nodes in a network
Random walk is an important tool in many graph mining applications including estimating graph parameters, sampling portions of the graph, and extracting dense communities. In this paper we consider the problem of sampling nodes from a large graph according to a prescribed distribution by using random walk as the basic primitive. Our goal is to obtain algorithms that make a small number of queries to the graph but output a node that is sampled according to the prescribed distribution. Focusing on the uniform distribution case, we study the query complexity of three algorithms and show a near-tight bound expressed in terms of the parameters of the graph such as average degree and the mixing time. Both theoretically and empirically, we show that some algorithms are preferable in practice than the others. We also extend our study to the problem of sampling nodes according to some polynomial function of their degrees; this has implications for designing efficient algorithms for applications such as triangle counting
COMPUTING APPROXIMATE CUSTOMIZED RANKING
As the amount of information grows and as users become more
sophisticated, ranking techniques become important building blocks
to meet user needs when answering queries. PageRank is one of the
most successful link-based ranking methods, which iteratively
computes the importance scores for web pages based on the importance scores of incoming pages. Due to its success, PageRank has been applied in a number of applications that require customization.
We address the scalability challenges for two types of customized
ranking. The first challenge is to compute the ranking of a
subgraph. Various Web applications focus on identifying a
subgraph, such as focused crawlers and localized search engines.
The second challenge is to compute online personalized ranking.
Personalized search improves the quality of search results for each
user. The user needs are represented by a personalized set of pages
or personalized link importance in an entity relationship graph.
This requires an efficient online computation.
To solve the subgraph ranking problem efficiently, we estimate the
ranking scores for a subgraph. We propose a framework of an exact
solution (IdealRank) and an approximate solution (ApproxRank) for
computing ranking on a subgraph. Both IdealRank and ApproxRank
represent the set of external pages with an external node
and modify the PageRank-style transition matrix with respect to . The IdealRank algorithm assumes that the scores of external pages are known. We prove that the IdealRank scores for pages in the subgraph converge to the true PageRank scores. Since the PageRank-style scores of external pages may not typically be available, we propose the ApproxRank algorithm to estimate scores for the subgraph. We analyze the distance between IdealRank scores and ApproxRank scores of the subgraph and show that it is within a
constant factor of the distance of the external pages. We demonstrate with real and synthetic data that ApproxRank provides a good approximation to PageRank for a variety of subgraphs.
We consider online personalization using ObjectRank; it is an
authority flow based ranking for entity relationship graphs. We formalize the concept of an aggregate surfer on a data graph; the surfer's behavior is controlled by multiple personalized rankings. We prove a linearity
theorem over these rankings which can be used as a tool to scale
this type of personalization. DataApprox uses a repository of precomputed rankings for a given set of link weights assignments. We define DataApprox as an optimization problem; it selects a subset of the precomputed rankings from the repository and produce a weighted combination of these rankings. We analyze the distance between the DataApprox scores and the real authority flow ranking scores and show that DataApprox has a theoretical bound. Our experiments on the DBLP data graph show that DataApprox performs well in practice and allows fast and accurate personalized authority flow ranking
How much is involved in DB publishing?
XML has been intensive investigated lately, with the sentence, that "XML is (has been) the standard form for data publishing", especially in data base area.That is, there are assumptions, that the newly published data take mostly the form of XML documents, particularly when databases are involved. This presumption seems to be the reason of the heavy investment applied for researching the topics of handling, querying and comprising XML documents. We check these assumptions by investigating the documents accessible on the Internet, possible going under the surface, into the "deep Web". The investigation involves analyzing large scientific databases, but the commercial data stored in the "deep Web" will be handled also.We used the technique of randomly generated IP addresses for investigating the "deep Web", i.e. the part of the Internet not indexed by the search engines. For the part of the Web that is accessed (indexed) by the large search engines we used the random walk technique to collect uniformly distributed samplings. We found, that XML has not(yet) been the standard of Web publishing, but it is strongly represented on the Web. We add a simple new evaluation method to the known uniformly sampling processes.These investigations can be repeated in the future in order to get a dynamic picture of the growing rate of the number of the XML documents present on the Web
A Comparison of Techniques for Sampling Web Pages
As the World Wide Web is growing rapidly, it is getting increasingly
challenging to gather representative information about it. Instead of crawling
the web exhaustively one has to resort to other techniques like sampling to
determine the properties of the web. A uniform random sample of the web would
be useful to determine the percentage of web pages in a specific language, on a
topic or in a top level domain. Unfortunately, no approach has been shown to
sample the web pages in an unbiased way. Three promising web sampling
algorithms are based on random walks. They each have been evaluated
individually, but making a comparison on different data sets is not possible.
We directly compare these algorithms in this paper. We performed three random
walks on the web under the same conditions and analyzed their outcomes in
detail. We discuss the strengths and the weaknesses of each algorithm and
propose improvements based on experimental results
VerdictDB: Universalizing Approximate Query Processing
Despite 25 years of research in academia, approximate query processing (AQP)
has had little industrial adoption. One of the major causes of this slow
adoption is the reluctance of traditional vendors to make radical changes to
their legacy codebases, and the preoccupation of newer vendors (e.g.,
SQL-on-Hadoop products) with implementing standard features. Additionally, the
few AQP engines that are available are each tied to a specific platform and
require users to completely abandon their existing databases---an unrealistic
expectation given the infancy of the AQP technology. Therefore, we argue that a
universal solution is needed: a database-agnostic approximation engine that
will widen the reach of this emerging technology across various platforms.
Our proposal, called VerdictDB, uses a middleware architecture that requires
no changes to the backend database, and thus, can work with all off-the-shelf
engines. Operating at the driver-level, VerdictDB intercepts analytical queries
issued to the database and rewrites them into another query that, if executed
by any standard relational engine, will yield sufficient information for
computing an approximate answer. VerdictDB uses the returned result set to
compute an approximate answer and error estimates, which are then passed on to
the user or application. However, lack of access to the query execution layer
introduces significant challenges in terms of generality, correctness, and
efficiency. This paper shows how VerdictDB overcomes these challenges and
delivers up to 171 speedup (18.45 on average) for a variety of
existing engines, such as Impala, Spark SQL, and Amazon Redshift, while
incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache
License.Comment: Extended technical report of the paper that appeared in Proceedings
of the 2018 International Conference on Management of Data, pp. 1461-1476.
ACM, 201
Deriving query suggestions for site search
Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files. © 2013 ASIS&T
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