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2018 will explore the challenges of macro-modeling with financial |+|
DSMM 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''' .
and economic datasets. The workshop will also showcase the '''Financial
Entity Identification and Information Integration (FEIII) Challenge'''.
dsfin/] | |
|−|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/] | |
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|−|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 | |
|−|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. | |
|−|For example, social media data often contains features that could enhance | |
|−|macroeconomic statistics derived from traditional survey-driven datasets. | |
|−|The resulting enriched datasets could explore hypotheses with a different | |
|−|focus or level of granularity. | |
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financial world is a closely interlinked Web of financial entities |+|
The of financial and , , and , , and . of tools for financial fiscal policy, 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 | |
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. | |
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|−|We expect attendees with an interest in information integration, data |+|
with , data , knowledge representation and .
|−|mining, knowledge representation , network and visual analytics, stream | |
|−|data processing, etc. | |
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|−|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 | |
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.
Past Proceedings are available here:  
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.