Big Data Analytics
With fast growing volumes of data in our world, the use of big data will become a key to accelerate productivity growth. This project investigates state-of-the-art techniques for mining massive data from various sources. We focus on structured (time series and event logs) and unstructured data (plain text, application traces, and system log files) mining. We are developing advanced analysis engines for mining time series data, complex event processing, graph mining, parallel and distributed mining, stream mining.
Individuals and businesses create tons of data every day. This data is generated by a variety of sources such as sensors monitoring our environment , IT infrastructure, physical and social infrastructure (e.g. power plants, transporation networks, smart cities), social media, transaction records of a web service, etc. Our Big Data" vision is to build an ecosystem that can handle the large volume and variety of data to extract knowledge from it. This knowledge can pertain to business intelligence, infrastructure management, public safety, health care, fraud detection, etc.
Our Big Data Analytics project aims to advance the state-of-the-art in data mining techniques for massive data. We focus on structured (time series and event logs) as well as unstructured data (plain text, application traces, and system log files) data mining. We have invented an NEC-award winning technology called Invariant Analyzer to extract hidden dependencies from various attributes in massive time series data. This Invariant Analyzer is extremely effective in helping operators manage large scale IT systems. Currently we are developing an advanced analysis engine for time series data that are collected from sensors monitoring complex physical systems. We are also working on problems of large scale events processing, graph mining, parallel and distributed data mining, stream data mining and so on.view all department projects