Representation Independent Analytics Over Structured Data

Database analytics algorithms leverage quantifiable structural properties of the data to predict interesting concepts and relationships. The same information, however, can be represented using many different structures and the structural properties observed over particular representations do not necessarily hold for alternative structures. Thus, there is no guarantee that current database analytics algorithms will still provide the correct insights, no matter what structures are chosen to organize the database. Because these algorithms tend to be highly effective over some choices of structure, such as that of the databases used to validate them, but not so effective with others, database analytics has largely remained the province of experts who can find the desired forms for these algorithms. We argue that in order to make database analytics usable, we should use or develop algorithms that are effective over a wide range of choices of structural organizations. We introduce the notion of representation independence, study its fundamental properties for a wide range of data analytics algorithms, and empirically analyze the amount of representation independence of some popular database analytics algorithms. Our results indicate that most algorithms are not generally representation independent and find the characteristics of more representation independent heuristics under certain representational shifts

Effective Entity Augmentation By Querying External Data Sources

Users often want to augment and enrich entities in their datasets with relevant information from external data sources. As many external sources are accessible only via keyword-search interfaces, a user usually has to manually formulate a keyword query that extract relevant information for each entity. This approach is challenging as many data sources contain numerous tuples, only a small fraction of which may contain entity-relevant information. Furthermore, different datasets may represent the same information in distinct forms and under different terms (e.g., different data source may use different names to refer to the same person). In such cases, it is difficult to formulate a query that precisely retrieves information relevant to an entity. Current methods for information enrichment mainly rely on lengthy and resource-intensive manual effort to formulate queries to discover relevant information. However, in increasingly many settings, it is important for users to get initial answers quickly and without substantial investment in resources (such as human attention). We propose a progressive approach to discovering entity-relevant information from external sources with minimal expert intervention. It leverages end users\u27 feedback to progressively learn how to retrieve information relevant to each entity in a dataset from external data sources. Our empirical evaluation shows that our approach learns accurate strategies to deliver relevant information quickly

Effective Ranking of XML Keyword Search Results

The popularity of XML has exacerbated the need for an easy-to-use, high precision query interface for XML data. When traditional document-oriented keyword search techniques do not suffice, natural language interfaces and keyword search techniques that take advantage of XML structure make it very easy for ordinary users to query XML databases. Unfortunately, current approaches to processing these queries rely heavily on heuristics that are intuitively appealing but ultimately ad hoc. These approaches often retrieve false positive answers, overlook correct answers, and cannot rank answers appropriately. To address these problems for data-centric XML, we propose {\it coherency ranking}, a domain- and database design-independent ranking method for XML keyword queries that is based on an extension of the concepts of data dependencies and mutual information. With coherency ranking, the results of a keyword query are invariant under schema reorganization. We analyze the way in which previous approaches to XML keyword search approximate coherency ranking, and present efficient algorithms to process queries and rank their answers using coherency ranking. Our empirical evaluation with two real-world XML data sets shows that coherency ranking has better precision and recall and provides better ranking than all previous approaches. Coherency ranking can also be used for keyword queries over relational and graph data