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DSMM 2017 == |+|
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|−|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. |+|
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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 of financial and , , and , , and and . to data , and . for financial and economic dataand economic policy, and .
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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'''. [https://ir.nist. gov/dsfin/] |+|
The DSMM workshop will explore theof data - and or datasets . will and .
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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 |+|
Latest revision as of 05:45, 21 January 2020
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.