TEEDA: An Interactive Platform for Matching Data Providers and Users in the Data Marketplace

Improvements in Web platforms for data exchange and trading are creating more opportunities for users to obtain data from data providers of different domains. However, the current data exchange platforms are limited to unilateral information provision from data providers to users. In contrast, there are insufficient means for data providers to learn what kinds of data users desire and for what purposes. In this paper, we propose and discuss the description items for sharing users’ calls for data as data requests in the data marketplace. We also discuss structural differences in data requests and providable data using variables, as well as possibilities of data matching. In the study, we developed an interactive platform, named “treasuring every encounter of data affairs” (TEEDA), to facilitate matching and interactions between data providers and users. The basic features of TEEDA are described in this paper. From experiments, we found the same distributions of the frequency of variables but different distributions of the number of variables in each piece of data, which are important factors to consider in the discussion of data matching in the data marketplace.

[1]  Yukio Ohsawa,et al.  Inferring variable labels using outlines of data in Data Jackets by considering similarity and co-occurrence , 2018, International Journal of Data Science and Analytics.

[2]  Xinwen Fu,et al.  A Survey on Big Data Market: Pricing, Trading and Protection , 2018, IEEE Access.

[3]  Jiguo Yu,et al.  Solving Data Trading Dilemma with Asymmetric Incomplete Information Using Zero-Determinant Strategy , 2018, WASA.

[4]  M. Ostrovsky Stability in Supply Chain Networks , 2005 .

[5]  Yukio Ohsawa,et al.  Understanding the Structural Characteristics of Data Platforms Using Metadata and a Network Approach , 2020, IEEE Access.

[6]  Ryutaro Ichise,et al.  Ontology Integration for Linked Data , 2014, Journal on Data Semantics.

[7]  K. J. Ray Liu,et al.  Data Trading With Multiple Owners, Collectors, and Users: An Iterative Auction Mechanism , 2017, IEEE Transactions on Signal and Information Processing over Networks.

[8]  Teruaki Hayashi,et al.  Development and Evaluation of a New Platform for Accelerating Cross-Domain Data Exchange and Cooperation , 2019, New Generation Computing.

[9]  Y. Ohsawa,et al.  Restructuring Incomplete Models in Innovators Marketplace on Data Jackets , 2017 .

[10]  Fan Wu,et al.  Achieving Data Truthfulness and Privacy Preservation in Data Markets , 2018, IEEE Transactions on Knowledge and Data Engineering.

[11]  Earl R. Babbie,et al.  The Basics Of Social Research , 1998 .

[12]  M. Newman Power laws, Pareto distributions and Zipf's law , 2005 .

[13]  Yingzhi Nie,et al.  Research on consumers’ protection in advantageous operation of big data brokers , 2018, Cluster Computing.

[14]  David R. Karger,et al.  Collaborative Data Analytics with DataHub , 2015, Proc. VLDB Endow..

[15]  Teppei Yagihashi Social Data Platform, D-Ocean , 2019, 2019 International Conference on Data Mining Workshops (ICDMW).

[16]  Eser Kandogan,et al.  LabBook: Metadata-driven social collaborative data analysis , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[17]  M. Boisot,et al.  Data, information and knowledge: have we got it right? , 2004 .

[18]  Yukio Ohsawa,et al.  Retrieval System for Data Utilization Knowledge Integrating Stakeholders' Interests , 2018, AAAI Spring Symposia.

[19]  Markus Spiekermann,et al.  Data Marketplaces: Trends and Monetisation of Data Goods , 2019, Intereconomics.

[20]  Wendy L. Tate,et al.  The use of secondary data in purchasing and supply management (P/SM) research , 2016 .

[21]  L. Shapley,et al.  College Admissions and the Stability of Marriage , 1962 .

[22]  Eiji Ikeda,et al.  Realization of Data Exchange and Utilization Society by Blockchain and Data Jacket: Merit of Consortium to Accelerate Co-Creation , 2018, 2018 IEEE International Conference on Data Mining Workshops (ICDMW).

[23]  Bernardo A. Huberman,et al.  A Market for Unbiased Private Data: Paying Individuals According to Their Privacy Attitudes , 2012, First Monday.

[24]  Maurizio Lenzerini,et al.  Semantic Characterization of Data Services through Ontologies , 2019, IJCAI.

[25]  J. Manyika Big data: The next frontier for innovation, competition, and productivity , 2011 .

[26]  Yukio Ohsawa,et al.  Data Jackets for Synthesizing Values in the Market of Data , 2013, KES.

[27]  Kim-Kwang Raymond Choo,et al.  SDTE: A Secure Blockchain-Based Data Trading Ecosystem , 2020, IEEE Transactions on Information Forensics and Security.

[28]  Shaojie Tang,et al.  An Online Pricing Mechanism for Mobile Crowdsensing Data Markets , 2017, MobiHoc.

[29]  Hiroshi Mano EverySense: An end-to-end IoT market platform , 2016, MobiQuitous.

[30]  Xuliang Duan,et al.  A Pricing Model for Big Personal Data , 2016 .

[31]  Ian Horrocks,et al.  Satisfaction and Implication of Integrity Constraints in Ontology-based Data Access , 2019, IJCAI.

[32]  Dan Suciu,et al.  Data Markets in the Cloud: An Opportunity for the Database Community , 2011, Proc. VLDB Endow..

[33]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[34]  David Geere,et al.  What is Your Data Worth , 2019 .

[35]  V. Crawford,et al.  Job Matching with Heterogeneous Firms and Workers , 1981 .

[36]  Diego Calvanese,et al.  Linking Data to Ontologies , 2008, J. Data Semant..

[37]  Teruaki Hayashi,et al.  Evaluation of Data Similarity using Data Jackets based on Users' Recognition , 2019, KES.

[38]  M. Katz Multisided Platforms, Big Data, and a Little Antitrust Policy , 2019, Review of Industrial Organization.

[39]  Data Matching , 2017, Encyclopedia of Machine Learning and Data Mining.