Difference between revisions of "DSMM: Data Science for Macro-Modeling with Financial and Economic Datasets"

Line 1: Line 1:
 
== DSMM 2017 ==
 
  
 
===Overview===
 
===Overview===

Revision as of 02:02, 16 November 2017

Overview

The promise of Big Data, linked data and social data is the availability of large scale yet granular datasets to support modeling of complex ecosystems reflecting cyber-human decision making. While complex data-driven models have emerged for climate modeling or systems biology, there has been less activity in macro-modeling with multiple heterogeneous economic or financial datasets. Such analytics requires dealing with multiple heterogeneous streams of data, each of which can be high in volume and variety and reflect varying degrees of veracity. The advent of Big Data infrastructures and analytical tools can support the required integration across these data sources, as well as macro-modeling with diverse datasets, and can potentially lead to the exploration of complex financial and economic ecosystems.

The financial world is a closely interlinked Web of financial entities and networks, supply chains and financial ecosystems. Financial analysts, regulators and academic researchers recognize they must address the unprecedented and unfamiliar challenges of monitoring, integrating, and analyzing such networks and ecosystems at scale. A researcher would have to process multiple heterogeneous data streams, extract relevant information, clean it, integrate information from distinct streams, perform entity resolution, and aggregate data before they can even begin their analysis. Doing all of this creates a high barrier for financial and economic data science at scale. The benefits of addressing these challenges are immense and may result in improved tools for regulators to monitor financial systems or to set economic or fiscal policy. Additional benefits may include fundamentally new designs of market mechanisms, new ways to reach consumers, and new ways to exploit the wisdom of the crowds.

The DSMM 2017 workshop will explore the challenges of data science for macro-modeling with financial and/or economic datasets. The workshop will also showcase the Financial Entity Identification and Information Integration (FEIII) at Scale Challenge. [1]

Proceedings of ACM DSMM 2014 are available here: http://dl.acm.org/citation.cfm?id=2630729. The Proceedings of ACM DSMM 2016 are available here: http://dl.acm.org/citation.cfm?id=2951894