WORKSHOP ON
BIG DATA MANAGEMENT: CHALLENGES AND OPPORTUNITIES
By
Dr. Sumeet Dua, Louisiana Tech University
July 2-4, 2013
Madanapalle Institute of Technology & Science
In today’s data rich but information poor technological age, automated data collection
instrumentation and processes have led to the tsunami of data stored in data marts and
storage structures, but majority of this data goes underutilized for information discovery.
Data analytics in such large data spaces has become increasingly challenging owing to
the growing volume, dimensionality, distribution, complexity and heterogeneity of the
underlying data elements. These data characteristics are broadening the lag between in
the data generation and its analytics, and eventual intelligence discovery. Owing to the
increasing number of services that adapt a data-centric view to information processing,
data-driven decision-making spurred by this access to large volumes of data has given
rise to the latest trend known as “Big Data.” With silos of exabytes of data being now
available for decision making - in the form of social media, network data, enterprise
operation and legacy databases etc., organizations are gearing to exploit the benefits of
data analytics on a large scale and tread using unconventional learning approaches. To
make this realization a reality, organizations are just not trying to scale up to much larger
datasets, but are focusing on design, development and implementation for techniques or
tools to manage and analyze this data, and perform predictive analytics faster than ever
before.
Big Data analytics is a multi step process that entails the systematic analysis of data
and its underlying at every step of the process – commonly referred to as the “Big Data
life cycle.” The Big Data life cycle is a derivative of the knowledge discovery in
databases (KDD) process with subtle but important enhancements. Data mining and
fusion has rapidly emerged as an enabling, robust, and scalable technique to analyze
data for novel patterns, trends, anomalies, structures, and features that can be
discovered from Big Data.
The focus of this workshop is two fold, namely, to highlight the challenges while
transcending towards Big Data, and the opportunities for training, design and
development of techniques and technologies that can used to overcome these
challenges. The workshop will discuss key data mining techniques, frameworks,
algorithms, and strategies as they are employed to analyze large volumes of data and its
application to intelligence discovery.
Topics: The topics covered in the workshop will include the following.

Big Data Management: Opportunities and Challenges;

Science of data analytics;

Data growth and associated computational complexity;

Algorithmic techniques of data mining;

The MapReduce framework and HADOOP;

Conventional Extract Transform Load (ETL) and Extract Load Transform (ELT)
for large data preprocessing;

Data preprocessing and transformation;

Dimensionality reduction methods;

Feature selection, distance metrics, algorithm design and analysis;

Data analytics using clustering, algorithms and frameworks;

Categories of clustering algorithms;

Data analytics using supervised learning and classification;

Multi-class classification;

Differences and shared challenges between classification and clustering;

Classification based models for clustering;

Spatio-temporal data structures for range queries for data mining applications;

Intricacies of image feature extraction for content-Based image retrieval;

Advanced techniques in indexing and querying large time-series;

Text mining for sentiment analysis;

Hands-on experience with data analytics.