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SIAM data mining "brings it" to annual meeting

Summary

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.
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Summary

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.

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LLTools: machine learning for human language processing

Summary

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 into larger applications. For the speech processing component, the pySLGR (Speaker, Language, Gender Recognition) tool provides signal processing, standard feature analysis, speech utterance embedding, and machine learning modeling methods in Python. The text processing component in LLTools extracts semantically meaningful insights from unstructured data via entity extraction, topic modeling, and document classification. The entity resolution component in LLTools provides approximate string matching, author recognition and graph-based methods for identifying and linking different instances of the same real-world entity. We show through two applications that LLTools can be used to rapidly create and train research prototypes for human language processing.
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Summary

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...

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Feedback-based social media filtering tool for improved situational awareness

Published in:
15th Annual IEEE Int. Symp. on Technologies for Homeland Security, HST 2016, 10-12 May 2016.

Summary

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 to update feature space selection and classifier construction across a multimodal set of diverse content characterization techniques. This approach is advantageous because the aspects of the data (or even the modalities of the data) that signify relevance cannot always be anticipated ahead of time. Experiments with both microblog text documents and coupled imagery and text documents demonstrate the effectiveness of this model on sample retrieval tasks, in comparison to more narrowly focused models operating in a priori selected feature spaces. The experiments also show that even relatively low feedback levels (i.e., tens of examples) can lead to a significant performance boost during the interactive retrieval process.
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Summary

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...

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Storage and Database Management for Big Data

Published in:
Big Data: Storage, Sharing, and Security

Summary

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 of database technologies in the past few years, there are still many fundamental considerations that must be made by scientists. For example, which of the seemingly infinite technologies are the best to use for my problem? Answers to such questions require careful understanding of the technology field in addition to the types of problems that are being solved. This chapter aims to address many of the pressing questions faced by individuals interesting in using sotrage or database technologies to solve their big data problems.
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Summary

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...

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Improved hidden clique detection by optimal linear fusion of multiple adjacency matrices

Published in:
2015 Asilomar Conf. on Signals, Systems and Computers, 8-11 November 2015.

Summary

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 given multiple Erdos-Renyi modeled adjacency matrices containing a common hidden or planted clique. The objective is to combine them linearly so that the principal eigenvectors of the resulting matrix best reveal the vertices associated with the clique. We utilize recent results from random matrix theory to derive the optimal weighting coefficients and use these insights to develop a data-driven fusion algorithm. We demonstrate the improved performance of the algorithm relative to other simple heuristics.
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Summary

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...

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Residuals-based subgraph detection with cue vertices

Published in:
2015 Asilomar Conf. on Signals, Systems and Computers, 8-11 November 2015.

Summary

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 results on subgraph detection when a cue vertex is provided, indicating one of the vertices in the community of interest. Several recent algorithms for local community detection are applied in this context, and we compare their empirical performance to that of the simple method used to derive the theoretical detection limits.
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Summary

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...

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Sampling operations on big data

Published in:
2015 Asilomar Conf. on Signals, Systems and Computers, 8-11 November 2015.

Summary

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 to compress signals are often undefined in data that is characterized by heterogeneity, high dimensionality, and lack of known structure. In this article, we describe and demonstrate an approach to sample large datasets such as social media data. We evaluate the effect of sampling on a common predictive analytic: link prediction. Our results indicate that greatly sampling a dataset can still yield meaningful link prediction results.
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Summary

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...

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Sampling large graphs for anticipatory analytics

Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.

Summary

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 more complex algorithms. We are investigating the use of sampling to mitigate these challenges, specifically sampling large graphs. Often, large datasets can be represented as graphs where data entries may be edges, and vertices may be attributes of the data. In particular, we present the results of sampling for the task of link prediction. Link prediction is a process to estimate the probability of a new edge forming between two vertices of a graph, and it has numerous application areas in understanding social or biological networks. In this paper we propose a series of techniques for the sampling of large datasets. In order to quantify the effect of these techniques, we present the quality of link prediction tasks on sampled graphs, and the time saved in calculating link prediction statistics on these sampled graphs.
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Summary

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...

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Using a power law distribution to describe big data

Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.

Summary

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 difficulty often wastes valuable researcher and computational time by expending resources on uninteresting parts of data. Social sensors, or sensors which produce data based on human activity, such as Wikipedia, Twitter, and Facebook have an underlying structure which can be thought of as having a Power Law distribution. Such a distribution implies that few nodes generate large amounts of data. In this article, we propose a technique to take an arbitrary dataset and compute a power law distributed background model that bases its parameters on observed statistics. This model can be used to determine the suitability of using a power law or automatically identify high degree nodes for filtering and can be scaled to work with big data.
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Summary

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...

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A spectral framework for anomalous subgraph detection

Published in:
IEEE Trans. Signal Process., Vol. 63, No. 16, 15 August 2015, 4191-4206.

Summary

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 the data. While the detection of such anomalous subgraphs has received a substantial amount of attention, no application-agnostic framework exists for analysis of signal detectability in graph-based data. In this paper, we describe a framework that enables such analysis using the principal eigenspace of a graph's residuals matrix, commonly called the modularity matrix in community detection. Leveraging this analytical tool, we show that the framework has a natural power metric in the spectral norm of the anomalous subgraph's adjacency matrix (signal power) and of the background graph's residuals matrix (noise power). We propose several algorithms based on spectral properties of the residuals matrix, with more computationally expensive techniques providing greater detection power. Detection and identification performance are presented for a number of signal and noise models, including clusters and bipartite foregrounds embedded into simple random backgrounds, as well as graphs with community structure and realistic degree distributions. The trends observed verify intuition gleaned from other signal processing areas, such as greater detection power when the signal is embedded within a less active portion of the background. We demonstrate the utility of the proposed techniques in detecting small, highly anomalous subgraphs in real graphs derived from Internet traffic and product co-purchases.
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Summary

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...

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