Ramoza Ahsan

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Finding similar trends among time series data is critical for applications ranging from financial planning to policy making. The detection of these multifaceted relationships, especially time warped matching of time series of different lengths and alignments is prohibitively expensive to compute. To achieve real time responsiveness on large time series(More)
Crucial to answering economic, social and political questions facing our society, data tends to be diverse and distributed through sites across the Internet. The creation of tools to integrate and analyze it is of paramount interest. Yet the automation of these processes continues to be a great challenge. Our work rests on the observation that a high number(More)
Association rule mining is known to be computationally intensive, yet real-time decisionmaking applications are increasingly intolerant to delays. The state-of-the-art PARAS 1 solution, a parameter space framework for online association mining, enables efficient rule mining by compactly indexing the final ruleset and providing efficient query-time(More)
This paper presents the challenges and problems faced in dynamic environment optimization problems. Real world problems are mostly dynamic thus such algorithms are required which can track the moving optima. Classical Optimization algorithm searches sequentially for the solution and are based on differential equations. In order to track the moving optima(More)
In an era where Big Data can greatly impact a broad population, many novel opportunities arise, chief among them the ability to integrate data from diverse sources and "wrangle" it to extract novel insights. Conceived as a tool that can help both expert and non-expert users better understand public data, MATTERS was collaboratively developed by the(More)
Traditional temporal association mining systems, once supplied with a specific parameter setting such as time periods of interest, minimum support and confidence, generate the rule set from scratch. This one-at-a-time paradigm forces the analysts to perform successive trial-and-error iterations to finally discover interesting temporal patterns. This process(More)
In an era where Big Data can greatly impact a broad population, many novel opportunities arise, chief among them the ability to integrate data from diverse sources and “wrangle” it to extract novel insights. Conceived as a tool that can help both expert and non-expert users better understand public data, MATTERS 1 was collaboratively developed by the(More)
Modern applications in this digital age collect a staggering amount of time series data from economic growth rates to electrical household consumption habits. To make sense of it, domain analysts interactively sift through these time series collections in search of critical relationships between and recurring patterns within these time series. The ONEX(More)
In the era of big data, economic competitiveness is assured by decision making leveraging insights gained from large scale yet granular data sets from a rich diversity of areas. In this light, the METIS system collaboratively developed by a team at WPI, the Massachusetts High Tech Council and other institutions, emerges as an analytic platform offering(More)
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