Workshop on Econometric Models, Machine Learning and Applications (EMMLA'16)

Rationale: An economic model is a theory of how different factors in the economy interact with one another. Econometric models are elegant, capture the interaction among different factors and can be mimicked to explain complex phenomena in nature and computing. Lately, such models in conjunction with Machine learning, graph theory, convex optimization and parametric statistical estimation gave birth to supremely competent models and are exploited in Market driven technological innovation. Enterprises are enhancing investments in cloud services setting up data centers to meet growing demand. A typical investment is of the order of millions of dollars, infrastructure and recurring cost included. The special session armed with scholarly tutorials aims to discuss algorithmic/analytical approaches to address the issues of optimal utilization of the resources towards a feasible and profitable model. The learning models through delivered lectures and contributed papers seek to answer questions on maximal revenue given a set of budgetary constraints to achieve target output. Such models are utilized to analyze complex problems in Physical and Information sciences as well. The session would deliberate on diverse applications of Econometric and learning models and hope to create and disseminate knowledge in basic and inter-disciplinary areas such as predictive estimation, pricing and revenue models, portfolio optimization, forecasting, utility and QoS parameters, parametric and nonparametric estimation, green computing and communications, sustainable modeling to name a few.

Salient and central theme of this workshop is to solicit and discuss recent work in constructing models using Econometrics and Data Science that helps in achieving following goals:

  1. To forecast the revenue with a given amount of investment or input cost.
  2. Analysis of maximum production such that total cost does not exceed a particular amount.
  3. Analysis of maximum profit that can be achieved.
  4. Analysis of minimum cost /input to obtain a certain output.
  5. Exploit human augmented computing

The objective is to empower the IaaS entrepreneurs (while establishing an IT data center) and Risk managers with several such decision theoretic tools to estimate the probable output, revenue, consumption and profit. Certainly it is directly related to a given amount of budget and its optimization. Thus, it deals with minimization of costs and maximization of profits too. This is a hot topic and has been gaining considerable traction in recent times.
Topics may include, but, are not limited to: 

  • Pricing Models in Cloud and Services Computing
  • Revenue Models
  • Scientometric Analysis and Astroinformatics
  • Cloud Data Analytics
  • Big Data on Clouds
  • Cloud Energy Consumption and Optimization
  • Data Center Optimization
  • Cost and Profit Modeling
  • Forecasting Methods
  • Predictive Analytics on Cloud and Services Computing
  • Mathematical Foundation of Services Computing
  • Web Services Modeling and Performance Management
  • Resource Acquisition Models in Utility Computing
  • Mathematical Foundation of Business Process Modeling
  • Scientometric Modeling and Ranking Algorithms
  • Machine Learning in Astronomy Data Analysis

Original and unpublished research manuscripts in the areas of algorithmic, mathematical, statistical and computational methods that are central in Cloud and services computing, Scientometric modeling and Astroinformatics are solicited. Authors should submit their papers online using EDAS. Unregistered authors should first create an account on EDAS to log on. Further guidelines for submission are posted at:

All papers that conform to submission guidelines will be peer reviewed and evaluated based on originality, technical and/or research content/depth, correctness, relevance to conference, contributions, and readability. The manuscripts should be submitted in PDF format. Acceptance of papers will be communicated to authors by email. At least one full paying author of each accepted paper must register for the conference before the indicated deadline. Accepted and presented papers will be published in the conference proceedings and submitted to IEEE Xplore as well as other Abstracting and Indexing (A&I) databases.

Workshop Organisers

Snehanshu Saha, PES Institute of Technology, Bangalore South Campus, India (Chair)
Anand Narasimhamurthy, BITS Hyderabad, India (Co-Chair)
Asif Ekbal, IIT Patna, India (Co-Chair)
Sriparna Saha, IIT Patna, India (Co-Chair)

TPC Members

Nithin Nagraj, NIAS
Shakti Mishra, MVIT
Jaya S,Nair, IIIT-B
Sirisha Rao, IIIT-B
Arvind Sharma, IIIT-H
Sakyajit Bhattacharya, XRCI
Aquila Khanam, Columbia University
Hima Patel, Shell R&D
Arijit Mukherjee, TCS Innovation Labs
Gowri Srinivasa, PESIT South
Bharath BN, PESIT South
B.S. DayaSagar, ISI Bangalore
Saroj Meher, ISI Bangalore


Paper Submission Ends: June 30, 2016
Acceptance Notification: July 20, 2016
Final Paper Deadline: August 20, 2016


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