The ObjectRank system applies authority-based ranking to keyword search in databases modeled as labeled graphs. Conceptually, authority originates at the nodes (objects) containing the keywords and flows to objects according to their semantic connections. Each node is ranked according to its authority with respect to the particular

Relational, XML and HTML data can be represented as graphs with entities as nodes and relationships as edges. Text is associated with nodes and possibly edges. Keyword search on such graphs has received much attention lately. A central problem in this scenario is to e#ciently extract from the data graph a small number of the "best" answer trees. A Backward Expanding search, starting at nodes matching keywords and working up toward confluent roots, is commonly used for predominantly text-driven queries. But it can perform poorly if some keywords match many nodes, or some node has very large degree. In this paper

The most acute information management challenges today stem from organizations relying on a large number of diverse, interrelated data sources, but having no means of managing them in a convenient, integrated, or principled fashion. These challenges arise in enterprise and government data management, digital libraries, “smart ” homes and personal information management. We have proposed dataspaces as a data management abstraction for these diverse applications and DataSpace Support Platforms (DSSPs) as systems that should be built to provide the required services over dataspaces. Unlike data integration systems, DSSPs do not require full semantic integration of the sources in order to provide useful services. This paper lays out specific technical challenges to realizing DSSPs and ties them to existing work in our field. We focus on query answering in DSSPs, the DSSP’s ability to introspect on its content, and the use of human attention to enhance the semantic relationships in a dataspace. 1.

With the amount of available text data in relational databases growing rapidly, the need for ordinary users to search such information is dramatically increasing. Even though the major RDBMSs have provided full-text search capabilities, they still require users to have knowledge of the database schemas and use a structured query language to search information. This search model is complicated for most ordinary users. Inspired by the big success of information retrieval (IR) style keyword search on the web, keyword search in relational databases has recently emerged as a new research topic. The differences between text databases and relational databases result in three new challenges: (1) Answers needed by users are not limited to individual tuples, but results assembled from joining tuples from multiple tables are used to form answers in the form of tuple trees. (2) A single score for each answer (i.e. a tuple tree) is needed to estimate its relevance to a given query. These scores are used to rank the most relevant answers as high as possible. (3) Relational databases have much richer structures than text databases. Existing IR strategies are inadequate in ranking relational outputs. In this paper, we propose a novel IR ranking strategy for effective keyword search. We are the first that conducts comprehensive experiments on search effectiveness using a real world database and a set of keyword queries collected by a major search company. Experimental results show that our strategy is significantly better than existing strategies. Our approach can be used both at the application level and be incorporated into a RDBMS to support keyword-based search in relational databases. 1.

Recently, there has been a great deal of interest in the development of techniques to evaluate path expressions over collections of XML documents. In general, these path expressions contain both structural and keyword components. Several methods have been proposed for processing path expressions over graph/tree-structured XML data. These methods can be classified into two broad classes. The first involves graph traversal where the input query is evaluated by traversing the data graph or some compressed representation. The other class involves information-retrieval style processing using inverted lists. In this framework, structure indexes have been proposed to be used as a substitute for graph traversal. These structure indexes are proven to be very effective when applied to queries that examine the “coarse ” structure of documents. For example, for many

With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly. In this paper, we study the effectiveness and the efficiency issues of answering top-k keyword query in relational database systems. We propose a new ranking formula by adapting existing IR techniques based on a natural notion of virtual document. Compared with previous approaches, our new ranking method is simple yet effective, and agrees with human perceptions. We also study efficient query processing methods for the new ranking method, and propose algorithms that have minimal accesses to the database. We have conducted extensive experiments on large-scale real databases using two popular RDBMSs. The experimental results demonstrate significant improvement to the alternative approaches in terms of retrieval effectiveness and efficiency. Categories and Subject Descriptors

This paper presents the novel SphereSearch Engine that provides unified ranked retrieval on heterogeneous XML and Web data. Its search capabilities include vague structure conditions, text content conditions, and relevance ranking based on IR statistics and statistically quantified ontological relationships.

Conventional keyword search engines are restricted to a given data model and cannot easily adapt to unstructured, semistructured or structured data. In this paper, we propose an efficient and adaptive keyword search method, called EASE, for indexing and querying large collections of heterogenous data. To achieve high efficiency in processing keyword queries, we first model unstructured, semi-structured and structured data as graphs, and then summarize the graphs and construct graph indices instead of using traditional inverted indices. We propose an extended inverted index to facilitate keyword-based search, and present a novel ranking mechanism for enhancing search effectiveness. We have conducted an extensive experimental study using real datasets, and the results show that EASE achieves both high search efficiency and high accuracy, and outperforms the existing approaches significantly.

Keyword search is a familiar and potentially effective way to find information of interest that is “locked ” inside relational databases. Current work has generally assumed that answers for a keyword query reside within a single database. Many practical settings, however, require that we combine tuples from multiple databases to obtain the desired answers. Such databases are often autonomous and heterogeneous in their schemas and data. This paper describes Kite, a solution to the keyword-search problem over heterogeneous relational databases. Kite combines schema matching and structure discovery techniques to find approximate foreign-key joins across heterogeneous databases. Such joins are critical for producing query results that span multiple databases and relations. Kite then exploits the joins – discovered automatically across the databases – to enable fast and effective querying over the distributed data. Our extensive experiments over real-world data sets show that (1) our query processing algorithms are efficient and (2) our approach manages to produce high-quality query results spanning multiple heterogeneous databases, with no need for human reconciliation of the different databases. 1

We study the problem of applying adaptive filters for approximate query processing in a distributed stream environment. We propose filter bound assignment protocols with the objective of reducing communication cost. Most previous works focus on value-based queries (e.g., average) with numerical error tolerance. In this paper, we cover entity-based queries (e.g., a nearest neighbor query returns object names rather than a single value). In particular, we study non-value-based tolerance (e.g., the answer to the nearest-neighbor query should rank third or above). We investigate different non-value-based error tolerance definitions and discuss how they are applied to two classes of entity-based queries: non-rankbased and rank-based queries. Extensive experiments show that our protocols achieve significant savings in both communication overhead and server computation. 1