Intelligent Big Data Analytics, Applications, and Systems

The UAlbany IDIAS Lab develops models, algorithms, and systems for solving problems involving big (networked) data. We work closely with domain experts to solve challenging problems in the study of Online Social Networks, Internet of Things, and Cyber-Physical Systems.

Research Areas

Using Big Data to Understand Social Behavior

Several models on the flow of information in online social networks have been proposed in the literature, such models assume that either the spreading process takes place on an unsigned network or that there is only one process unfolding over the network of ties. In reality however, people trying to decide whether to adopt an innovation, a political idea, or a product are frequently influenced by a variety of factors. We have introduced and continue to develop a unified model that enables the realistic modelling and accurate prediction of diffusion in networks.

SocialCom 2011; ASONAM 2012 (2); SocialCom 2012; JCI 2013; MSM 2013; TOIS 2013; SNAM 2013; ASONAM 2013 (2); SNAM 2014; ASONAM 2014; SNAM 2015; ASONAM 2015; WWW 2015; SNAM 2016; NetSci-X 2016

IoT-Generated Big Data Informatics

Recent studies have shown that 2.5 quintillion bytes of data is being generated per day and this is set to explode by 2020. Much of this data is and will be generated from Internet of Things (IoT) devices such as sensors in Smart Cities and Smart Energy Grids, and smart consumer appliances or social media. To address the challenges associated with the real--time exploration, mining, and analytics of big data, we are working towards developing novel capabilities that will facilitate the integration and querying of numerous and dynamic data streams, large repositories of historical data, and static knowledge to support meaningful pattern and connection discovery for near real-time decision making.

IGCC 2014; Big Data 2014 (2); e-Energy 2015 (3); IGI 2015; Big Data 2015; AAAI 2015 (2); SPE IE 2016; FTC 2016; ICCS 2016; TPDS 2016; AAAI 2016; IJCAI 2016

Relative Importance of Nodes in Networks

In graph theory, identifying the most important vertices within a graph implies the use of a centrality metric. Applications include identifying the influential users in a social network, key infrastructure nodes in the Internet, and disease spreaders. Over the years, a variety of centrality metrics have been proposed to measure the relative importance of nodes based on the network structure. Motivated by the need to identify "influential" nodes and study their role in the flow of diseases in epidemiology and information in social networks, we have devised and continue to develop niche centrality metrics based on diffusion dynamics over networks.

SNAM 2016

Big Data with Structure on Hardware Steroids

Computational resources have been unable to keep up with the vast amounts of data which keep piling up at staggering velocities. Yet, querying and analysis of Big Data has become critical for data-enabled scientific discovery. To address the dark silicon challenge, we are pairing conventional CPUs with massively parallel accelerators such as field-programmable gate arrays (FPGA) and emerging memory technologies and developing novel energy-efficient and massive parallel algorithms capable of handling not only the size, and rate of Big Data, but also the corresponding heterogeneity in the compute fabric and the memory hierarchy.

ReConFig 2015 (2); IPDPS 2015 (2); CCGrid 2015; FCCM 2016