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

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[https://ir.nist.gov/feiii/]
[https://ir.nist.gov/feiii/]


Past Proceedings are available here: [https://dl.acm.org/doi/10.1145/3299869.3323599 2013] [https://dl.acm.org/citation.cfm?id=3336499 2015]
Past Proceedings are available here:  
[https://dl.acm.org/citation.cfm?id=3220547 2016] [https://dl.acm.org/citation.cfm?id=3077240 2017]
[https://dl.acm.org/doi/proceedings/10.1145/3401832 2020]
[https://dl.acm.org/citation.cfm?id=2951894 2018] [https://dl.acm.org/citation.cfm?id=2630729 2019]. [https://dl.acm.org/doi/proceedings/10.1145/3401832 2020]
[https://dl.acm.org/doi/10.1145/3299869.3323599 2019] [https://dl.acm.org/citation.cfm?id=3336499 2018]
 
[https://dl.acm.org/citation.cfm?id=3220547 2017] [https://dl.acm.org/citation.cfm?id=3077240 2016]
[https://dl.acm.org/citation.cfm?id=2951894 2015] [https://dl.acm.org/citation.cfm?id=2630729 2013].  
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 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.  



Revision as of 02:05, 17 June 2020

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COVID-19 UPDATE

Due to the COVID-19 outbreak, SIGMOD 2020 will be an online event. In response, DSMM will be scheduled as an online event on June 14th starting at 11 a.m. EASTERN TIME. Call in details and the schedule are here:

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: 2020 2019 2018 2017 2016 2015 2013. 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.