DSMM: Data Science for Macro-Modeling with Financial and Economic Datasets
From datascience
(Redirected from Main Page)
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 2014. 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.