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The emerging data mining tools and systems lead naturally to the demand of a powerful data mining query language, on top of which many i n teractive and exible graphical user interfaces can be developed. This motivates us to design a data mining query language, DMQL, for mining diierent kinds of knowledge in relational databases. Portions of the proposed(More)
Financial markets are quite sensitive to unanticipated news and events. Identifying the effect of news on the market is a challenging task. In this demo, we present Forex-foreteller (FF) which mines news articles and makes forecasts about the movement of foreign currency markets. The system uses a combination of language models, topic clustering, and(More)
Recent tremendous technical advances in processing power, storage capacity, and interconnectivity is creating unprecedented quantities of digital data. Data mining, the science of extracting useful knowledge from such huge data repositories, has emerged as a young and interdisciplinary field in computer science. Data mining techniques have been widely(More)
We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future(More)
1 Introduction Recent tremendous technical advances in processing power, storage capacity, and inter-connectivity of computer technology is creating unprecedented quantities of digital data. Data mining, the science of extracting useful knowledge from such huge data repositories, has emerged as a young and interdisciplinary field in computer science. Data(More)
The series publishes 50-to 150-page publications on topics pertaining to data mining, web mining, text mining, and knowledge discovery, including tutorials and case studies. The scope will largely follow the purview of premier computer science conferences, such as KDD. Potential topics include, but not limited to, data mining algorithms, innovative data(More)
Active research has been conducted on knowledge discovery in databases by the researchers in our group for years, with many i n teresting results published and a prototyped knowledge discovery system, DBMiner previously called DBLearn, developed and demonstrated in several conferences. Our research c o v ers a wide spectrum of knowledge discovery, including(More)
Although exercise is recognized as a powerful tool to combat obesity, remarkably few US adults pursue adequate amounts of exercise, with one major impediment being a lack of motivation for active behaviors. Recent empirical work has demonstrated that behavior can be guided by goals to be generally active or inactive. In the present paper, an experiment is(More)
—As a surrogate data source for many real-world phenomena, social media such as Twitter can yield key insight into people's behavior and their group affiliations and memberships. As an event unfolds on Twitter, the language, hashtags, and vocabulary used to describe it evolves over time, so that it is difficult to a priori capture the composition of a(More)