DSMM 2014 - Workshop on Data Science for Macro-Modeling

Workshop on Data Science for Macro-Modeling DSMM2014
dsmm2014.org
Held in conjunction with ACM SIGMOD 2014
Friday June 27, 2014
The increasing availability of Open Data from a variety of sources including the Web, social media and the government, in conjunction with the growth of Big Data infrastructures and analytics tools, provides the ability to model complex ecosystems enabling cyber-human decision making. While data-driven models have emerged for a range of challenges from climate modeling to systems biology to personalized medicine, there has been relatively, little activity in macro-modeling using multiple heterogeneous financial and economic datasets.
The real promise of Open Data and Big Data lies in the dramatically increased value gained from integrating data from multiple sources, as illustrated by the following example: The systemic risks associated with the subprime lending market and the crash of the housing market in 2007 could have been modeled through a comprehensive integration and analysis of available public datasets. For example, the datasets relevant to the home mortgage supply chain include the following: (a) regulatory documents made available by MBS issuers, publicly traded financial institutions and mutual funds; (b) subscription-based third party datasets on underlying mortgages; (c) individual home transaction data such as sales, foreclosure and tax records; (d) local economic data such as employment and income-levels; (e) financial news articles. Integrating these datasets may have provided financial analysts, regulators and academic researchers, with comprehensive models to enable risk assessment.
Economists have been the leaders in creating longitudinal panel datasets and have had a successful history of using national datasets from the Census Bureau, the Department of Labor, etc., and global datasets from the UN, World Bank, etc. Here, too, there has been much less activity in modeling that integrated multiple heterogeneous datasets. While integrating datasets may pose technical, policy and privacy challenges, the potential benefits are immense. For example, social media data often contains features that could enhance macroeconomic statistics derived from traditional survey-driven datasets. Enriching longitudinal panel datasets with social media could explore hypotheses with a different focus or level of granularity; for example, one could study the decision making of individuals whose social media profiles would reflect their beliefs, intent, interests, sentiments, opinions, and state of mind.
This workshop will explore the challenges of data science for macro-modeling with financial and/or economic datasets. We expect a mix of paper submissions and attendees with an interest in information integration, data mining, knowledge representation, stream data processing, etc. A small number of domain specialists from finance and economics are also expected to attend.
IMPORTANT DATES
Submission deadline: Friday March 21, 2014.
Notification to authors: Monday April 28, 2014.
Camera-ready due: Friday May 16, 2014.
Workshop: Friday June 27, 2014.
SUBMISSION FORMAT
We will accept the following types of papers in the SIGMOD format:
â?¢ Regular papers that are a maximum of 6 pages will have a presentation slot.
â?¢ Extended abstracts of up to 2 pages will have a poster presentation and a short presentation slot if time permits.
SUBMISSION SITE https://cmt.research.microsoft.com/DSMM2014/
PROGRAM CHAIRS
Rajasekar Krishnamurthy IBM Research rajase-AT-us.ibm.com
Louiqa Raschid University of Maryland louiqa-AT-umiacs.umd.edu
Shiv Vaithyanathan IBM Research vaithyan-AT-us.ibm.com
STEERING COMMITTEE
Lise Getoor UC Santa Cruz getoor-AT-soe.ucsc.edu
Laura Haas IBM Research lmhaas-AT-us.ibm.com
H.V. Jagadish University of Michigan jag-AT-umich.edu
PROGRAM COMMITTEE
Richard Anderson Lindenwood University rganderson.stl-AT-gmail.com
Michael Cafarella University of Michigan michjc-AT-umich.edu
Sanjiv Das Santa Clara University srdas-AT-scu.edu
Amol Deshpande University of Maryland amol-AT-cs.umd.edu
Mark Flood Office of Financial Research
mark.flood-AT-treasury.gov
Juliana Freire New York University juliana.freire-AT-nyu.edu
Gerard Hoberg University of Maryland ghoberg-AT-rhsmith.umd.edu
Vasant Honavar Pennsylvania State U vhonavar-AT-ist.psu.edu
Joe Langsam University of Maryland jlangsam-AT-rhsmith.umd.edu
Shawn Mankad University of Maryland smankad-AT-rhsmith.umd.edu
Felix Naumann Hasso Plattner Institute, Germany felix.naumann-AT-hpi.uni-potsdam.de
Frank Olken National Science Foundation folken-AT-nsf.gov
Christopher Re Stanford University chrismre-AT-cs.stanford.edu
WEBMASTER
Peratham Wiriyathammabhum University of Maryland peratham-AT-cs.umd.edu