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

 
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===Overview===
 
===Overview===
  
DSMM 2018 will explore the challenges of macro-modeling with financial and economic datasets. The workshop will also showcase the '''Financial Entity Identification and Information Integration (FEIII) Challenge'''.
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DSMM 2020 will explore the challenges of macro-modeling with financial and socio-economic datasets. The workshop will also showcase the '''Financial Entity Identification and Information Integration (FEIII) Challenge''' and will involve a challenge task over small business data.
[https://ir.nist.gov/dsfin/]
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[https://ir.nist.gov/feiii/]
Financial big data and FINTECH applications are in the vanguard of activities around the deployment of Open Knowledge Networks.
 
[http://ichs.ucsf.edu/open-knowledge-network/]
 
  
Past proceedings are available here: [https://dl.acm.org/citation.cfm?id=3077240]
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Past Proceedings are available here: [https://dl.acm.org/doi/10.1145/3299869.3323599] [https://dl.acm.org/citation.cfm?id=3336499]
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[https://dl.acm.org/citation.cfm?id=3220547] [https://dl.acm.org/citation.cfm?id=3077240]
 
[https://dl.acm.org/citation.cfm?id=2951894] [https://dl.acm.org/citation.cfm?id=2630729].
 
[https://dl.acm.org/citation.cfm?id=2951894] [https://dl.acm.org/citation.cfm?id=2630729].
  
DSMM 2018 will explore the challenges of macro-modeling with financial and economic datasets. The workshop will also showcase the Financial Entity Identification and Information Integration (FEIII) Challenge: [https://ir.nist.gov/dsfin] Financial big data and FINTECH applications are in the vanguard of
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The engines of commerce and industry continuously generate rich heterogeneous data that reflect financial and economic activity. Unfortunately, this complex data is often not captured or curated in machine understandable form, or readily integrated across resources and data streams, presenting an obstacle for research, policy and industry use. The Business Open Knowledge Network (BOKN) is an effort to harness and exploit this data. BOKN is envisioned as a shared resource of curated knowledge, with tools to support large-scale data analysis, and interfaces to allow access to additional repositories. BOKN will create unprecedented opportunities for financial and socio-economic research, will inform data-driven fiscal and economic policy, and will empower innovators and entrepreneurs.
activities around the deployment of Open Knowledge Networks:
 
[http://ichs.ucsf.edu/open-knowledge-network/]
 
  
Two trends are providing data-rich opportunities for macro-modeling of financial and economic ecosystems. First, public data is becoming increasingly available from a variety of sources (CRSP, EDGAR, FRED). Economists have used longitudinal datasets (US Census, Labor, etc.) to drive econometric and statistical research. The advent of Big Data infrastructures and analytical tools can support the required
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The DSMM workshop will explore technical challenges relevant to BOKN which includes combining state-of-the-art computational approaches for extracting, representing, linking, and analyzing data with complex and nuanced knowledge about the business domain. Domain-specific tools can leverage a wealth of unstructured data on the Web, as well as semi- structured data and time series datasets provided for regulatory or legal purposes, and reference datasets with standard identifiers and metadata that enable cross-resource federation. BOKN will include a hybrid knowledge graph that supports traditional symbolic knowledge representation enhanced by high-dimensional vector space embeddings capturing temporal evolution and semantic relationships that support machine learning applications.  
information fusion, as well as macro-modeling with diverse datasets, and can potentially lead to the exploration of complex financial and economic ecosystems.  Although integrating datasets may pose technical and policy/privacy challenges, the potential benefits are immense. The resulting enriched datasets could explore hypotheses with a different focus or level of granularity.
 
 
 
The financial world is a closely interlinked Web of financial entities and networks, supply chains and ecosystems. Analysts, regulators and researchers must address the challenges of monitoring, integrating, and analyzing at scale.  Technical challenges include entity identification; entity classification; learning relationships among entities. The benefits of addressing these challenges may result in improved tools for
 
regulators to monitor financial systems or to set fiscal policy.  Additional benefits may include fundamentally new designs of market mechanisms, new ways to reach consumers and to exploit the wisdom of the crowds.
 
  
 
We expect attendees with an interest in information integration, data mining, knowledge representation, network and visual analytics, stream data processing, etc. to participate.
 
We expect attendees with an interest in information integration, data mining, knowledge representation, network and visual analytics, stream data processing, etc. to participate.

Latest revision as of 05:45, 21 January 2020

Sponsor images2.png

Overview

DSMM 2020 will explore the challenges of macro-modeling with financial and socio-economic datasets. The workshop will also showcase the Financial Entity Identification and Information Integration (FEIII) Challenge and will involve a challenge task over small business data. [1]

Past Proceedings are available here: [2] [3] [4] [5] [6] [7].

The engines of commerce and industry continuously generate rich heterogeneous data that reflect financial and economic activity. Unfortunately, this complex data is often not captured or curated in machine understandable form, or readily integrated across resources and data streams, presenting an obstacle for research, policy and industry use. The Business Open Knowledge Network (BOKN) is an effort to harness and exploit this data. BOKN is envisioned as a shared resource of curated knowledge, with tools to support large-scale data analysis, and interfaces to allow access to additional repositories. BOKN will create unprecedented opportunities for financial and socio-economic research, will inform data-driven fiscal and economic policy, and will empower innovators and entrepreneurs.

The DSMM workshop will explore technical challenges relevant to BOKN which includes combining state-of-the-art computational approaches for extracting, representing, linking, and analyzing data with complex and nuanced knowledge about the business domain. Domain-specific tools can leverage a wealth of unstructured data on the Web, as well as semi- structured data and time series datasets provided for regulatory or legal purposes, and reference datasets with standard identifiers and metadata that enable cross-resource federation. BOKN will include a hybrid knowledge graph that supports traditional symbolic knowledge representation enhanced by high-dimensional vector space embeddings capturing temporal evolution and semantic relationships that support machine learning applications.

We expect attendees with an interest in information integration, data mining, knowledge representation, network and visual analytics, stream data processing, etc. to participate.