Applications in which plain text coexists with structured data are pervasive. Commercial relational database management systems (RDBMSs) generally provide querying capabilities for text attributes that incorporate state-of-the-art information retrieval (IR) relevance ranking strategies, but this search functionality requires that queries specify the exact column or columns against which a given list of keywords is to be matched. This requirement can be cumbersome and inflexible from a user perspective: good answers to a keyword query might need to be "assembled" -in perhaps unforeseen ways-by joining tuples from multiple relations. This observation has motivated recent research on free-form keyword search over RDBMSs. In this paper, we adapt IR-style document-relevance ranking strategies to the problem of processing free-form keyword queries over RDBMSs. Our query model can handle queries with both AND and OR semantics, and exploits the sophisticated single-column text-search functionality often available in commercial RDBMSs. We develop query-processing strategies that build on a crucial characteristic of IR-style keyword search: only the few most relevant matches -according to some definition of "relevance"-are generally of interest. Consequently, rather than computing all matches for a keyword query, which leads to inefficient executions, our techniques focus on the top-k matches for the query, for moderate values of k. A thorough experimental evaluation over real data shows the performance advantages of our approach. *

Many applications require finding objects closest to a specified location that contains a set of keywords. For example, online yellow pages allow users to specify an address and a set of keywords. In return, the user obtains a list of businesses whose description contains these keywords, ordered by their distance from the specified address. The problems of nearest neighbor search on spatial data and keyword search on text data have been extensively studied separately. However, to the best of our knowledge there is no efficient method to answer spatial keyword queries, that is, queries that specify both a location and a set of keywords.In this work, we present an efficient method to answer top-k spatial keyword queries. To do so, we introduce an indexing structure called IR2-Tree (Information Retrieval R-Tree) which combines an R-Tree with superimposed text signatures. We present algorithms that construct and maintain an IR2-Tree, and use it to answer top-k spatial keyword queries. Our algorithms are experimentally compared to current methods and are shown to have superior performance and excellent scalability.

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We study the problem of correlating micro-blogging activity with stock-market events, defined as changes in the price and traded volume of stocks. Specifically, we collect messages related to a number of companies, and we search for correlations between stock-market events for those companies and features extracted from the microblogging messages. The features we extract can be categorized in two groups. Features in the first group measure the overall activity in the micro-blogging platform, such as number of posts, number of re-posts, and so on. Features in the second group measure properties of an induced interaction graph, for instance, the number of connected components, statistics on the degree distribution, and other graph-based properties.We present detailed experimental results measuring the correlation of the stock market events with these features, using Twitter as a data source. Our results show that the most correlated features are the number of connected components and the number of nodes of the interaction graph. The correlation is stronger with the traded volume than with the price of the stock. However, by using a simulator we show that even relatively small correlations between price and micro-blogging features can be exploited to drive a stock trading strategy that outperforms other baseline strategies.

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The purpose of the current feasibility study was to examine the use, utility, and areas for refinement of a newly developed web-based and Android™ application (app) (i.e., CareHeroes) with multiple features to support individuals caring for loved ones with Alzheimer’s disease or other forms of dementia (AD). The study was performed over an 11-week period with triads of AD caregivers, assigned home care case managers, and primary care providers (PCP). The study involved quantitative and qualitative methodologies. Eleven AD caregivers (seven daughters, two sons, and two spouses), six case managers, and five PCPs participated. Data demonstrate participants were mostly satisfied with the multiple features and ability to access and use CareHeroes. Barriers for use include concerns about time constraints and not being familiar with technology. Although the study findings are promising, a longer term study to evaluate the impact of the CareHeroes app is indicated.

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