Publications

The Data Mining Activity Group is one of SIAM's most vibrant and dynamic activity groups. To better share our enthusiasm for data mining with the broader SIAM community, our activity group organized six minisymposia at the 2016 Annual Meeting. These minisymposia included 48 talks organized by 11 SIAM members.

Machine learning methods in Human Language Technology have reached a stage of maturity where widespread use is both possible and desirable. The MIT Lincoln Laboratory LLTools software suite provides a step towards this goal by providing a set of easily accessible frameworks for incorporating speech, text, and entity resolution components...

This paper describes a feature-rich model of data relevance, designed to aid first responder retrieval of useful information from social media sources during disasters or emergencies. The approach is meant to address the failure of traditional keyword-based methods to sufficiently suppress clutter during retrieval. The model iteratively incorporates relevance feedback...

The ability to collect and analyze large amounts of data is a growing problem within the scientific community. The growing gap between data and user calls for innovative tools that address the challenges faced by big data volume, velocity, and verity. While there has been great progress in the world...

Graph fusion has emerged as a promising research area for addressing challenges associated with noisy, uncertain, multi-source data. While many ad-hoc graph fusion techniques exist in the current literature, an analytical approach for analyzing the fundamentals of the graph fusion problem is lacking. We consider the setting where we are...

A common problem in modern graph analysis is the detection of communities, an example of which is the detection of a single anomalously dense subgraph. Recent results have demonstrated a fundamental limit for this problem when using spectral analysis of modularity. In this paper, we demonstrate the implication of these...

The 3Vs -- Volume, Velocity and Variety -- of Big Data continues to be a large challenge for systems and algorithms designed to store, process and disseminate information for discovery and exploration under real-time constraints. Common signal processing operations such as sampling and filtering, which have been used for decades...

The characteristics of Big Data - often dubbed the 3V's for volume, velocity, and variety - will continue to outpace the ability of computational systems to process, store, and transmit meaningful results. Traditional techniques for dealing with large datasets often include the purchase of larger systems, greater human-in-the-loop involvement, or...

The gap between data production and user ability to access, compute and produce meaningful results calls for tools that address the challenges associated with big data volume, velocity and variety. One of the key hurdles is the inability to methodically remove expected or uninteresting elements from large data sets. This...

A wide variety of application domains is concerned with data consisting of entities and their relationships or connections, formally represented as graphs. Within these diverse application areas, a common problem of interest is the detection of a subset of entities whose connectivity is anomalous with respect to the rest of...