How closely related are two nodes in a graph? How to compute this score quickly, on huge, disk-resident, real graphs? Random walk with restart (RWR) provides a good relevance score between two nodes in a weighted graph, and it has been successfully used in numerous settings, like automatic captioning of images, generalizations to the "connection subgraphs", personalized PageRank, and many more. However, the straightforward implementations of RWR do not scale for large graphs, requiring either quadratic space and cubic pre-computation time, or slow response time on queries. We propose fast solutions to this problem. The heart of our approach is to exploit two important properties shared by many real graphs: (a) linear correlations and (b) block- wise, community-like structure. We exploit the linearity by using low-rank matrix approximation, and the community structure by graph partitioning, followed by the Sherman- Morrison lemma for matrix inversion. Experimental results on the Corel image and the DBLP dabasets demonstrate that our proposed methods achieve significant savings over the straightforward implementations: they can save several orders of magnitude in pre-computation and storage cost, and they achieve up to 150x speed up with 90%+ quality preservation.

/pdf/fast-random-walk-with-restart-and-its-applications-78dshxwvmx.pdf

Fast Random Walk with Restart and Its Applications

With the growth of the Web, there has been a rapid increase in the number of users who need to access online databases without having a detailed knowledge of the schema or of query languages; even relatively simple query languages designed for non-experts are too complicated for them. We describe BANKS, a system which enables keyword-based search on relational databases, together with data and schema browsing. BANKS enables users to extract information in a simple manner without any knowledge of the schema or any need for writing complex queries. A user can get information by typing a few keywords, following hyperlinks, and interacting with controls on the displayed results. BANKS models tuples as nodes in a graph, connected by links induced by foreign key and other relationships. Answers to a query are modeled as rooted trees connecting tuples that match individual keywords in the query. Answers are ranked using a notion of proximity coupled with a notion of prestige of nodes based on inlinks, similar to techniques developed for Web search. We present an efficient heuristic algorithm for finding and ranking query results.

/pdf/keyword-searching-and-browsing-in-databases-using-banks-3ktuauorb3.pdf

Keyword searching and browsing in databases using BANKS

https://pure.mpg.de/pubman/item/item_1819068_3/component/file_1840695/MPI-I-2007-5-003.pdf

YAGO: A Large Ontology from Wikipedia and WordNet

DISCOVER operates on relational databases and facilitates information discovery on them by allowing its user to issue keyword queries without any knowledge of the database schema or of SQL. DISCOVER returns qualified joining networks of tuples, that is, sets of tuples that are associated because they join on their primary and foreign keys and collectively contain all the keywords of the query. DISCOVER proceeds in two steps. First the Candidate Network Generator generates all candidate networks of relations, that is, join expressions that generate the joining networks of tuples. Then the Plan Generator builds plans for the efficient evaluation of the set of candidate networks, exploiting the opportunities to reuse common subexpressions of the candidate networks.

We prove that DISCOVER finds without redundancy all relevant candidate networks, whose size can be data bound, by exploiting the structure of the schema. We prove that the selection of the optimal execution plan (way to reuse common subexpressions) is NP-complete. We provide a greedy algorithm and we show that it provides near-optimal plan execution time cost. Our experimentation also provides hints on tuning the greedy algorithm.

/pdf/discover-keyword-search-in-relational-databases-33fmpg1hdg.pdf

Discover: keyword search in relational databases

We consider the problem of efficiently producing ranked results for keyword search queries over hyperlinked XML documents. Evaluating keyword search queries over hierarchical XML documents, as opposed to (conceptually) flat HTML documents, introduces many new challenges. First, XML keyword search queries do not always return entire documents, but can return deeply nested XML elements that contain the desired keywords. Second, the nested structure of XML implies that the notion of ranking is no longer at the granularity of a document, but at the granularity of an XML element. Finally, the notion of keyword proximity is more complex in the hierarchical XML data model. In this paper, we present the XRANK system that is designed to handle these novel features of XML keyword search. Our experimental results show that XRANK offers both space and performance benefits when compared with existing approaches. An interesting feature of XRANK is that it naturally generalizes a hyperlink based HTML search engine such as Google. XRANK can thus be used to query a mix of HTML and XML documents.

/pdf/xrank-ranked-keyword-search-over-xml-documents-2xbmgcfuam.pdf

XRANK: ranked keyword search over XML documents

The BANKS system enables keyword-based search on databases, together with data and schema browsing. BANKS enables users to extract information in a simple manner without any knowledge of the schema or any need for writing complex queries. A user can get information by typing a few keywords, following hyperlinks, and interacting with controls on the displayed results. Extensive support for answer ranking forms a critical part of the BANKS system.

/pdf/banks-browsing-and-keyword-searching-in-relational-databases-ngenz5prn4.pdf

BANKS: browsing and keyword searching in relational databases

The cost of a query plan depends on many parameters, such as predicate selectivities and available memory, whose values may not be known at optimization time. Parametric query optimization (PQO) optimizes a query into a number of candidate plans, each optimal for some region of the parameter space.

We first propose a solution for the PQO problem for the case when the cost functions are linear in the given parameters. This solution is minimally intrusive in the sense that an existing query optimizer can be used with minor modifications: the solution invokes the conventional query optimizer multiple times, with different parameter values.

We then propose a solution for the PQO problem for the case when the cost functions are piecewise-linear in the given parameters. The solution is based on modification of an existing query optimizer. This solution is quite general, since arbitrary cost functions can be approximated to piecewise linear form. Both the solutions work for an arbitrary number of parameters.

/pdf/parametric-query-optimization-for-linear-and-piecewise-58n5wcz0u6.pdf

Parametric query optimization for linear and piecewise linear cost functions

The cost of a query plan depends on many parameters, such as predicate selectivities and available memory, whose values may not be known at optimization time. Parametric query optimization (PQO) optimizes a query into a number of candidate plans, each optimal for some region of the parameter space. We propose a heuristic solution for the PQO problem for the case when the cost functions may be nonlinear in the given parameters. This solution is minimally intrusive in the sense that an existing query optimizer can be used with minor modifications. We have implemented the heuristic and the results of the tests on the TPCD benchmark indicate that the heuristic is very effective. The minimal intrusiveness, generality in terms of cost functions and number of parameters and good performance (up to 4 parameters) indicate that our solution is of significant practical importance.

/pdf/anipqo-almost-non-intrusive-parametric-query-optimization-20x7rvki5a.pdf

Arvind Hulgeri

Papers

Keyword searching and browsing in databases using BANKS

BANKS: browsing and keyword searching in relational databases

Parametric query optimization for linear and piecewise linear cost functions

AniPQO: almost non-intrusive parametric query optimization for nonlinear cost functions

Keeyword Search in Databases.