Standardization Requirements Analysis on Big Data in Public Sector based on Potential Business Models

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1 , pp Standardization Requirements Analysis on Big Data in Public Sector based on Potential Business Models Suwook Ha 1, Seungyun Lee 2 and Kangchan Lee 3* 1,2,3 ETRI Abstract In recent years, according to the exponential growth of data, Big data is the key issue for data application. Many companies and government agencies are trying to adopt Big Data technologies for finding a new way of problem solving. The Korean government concentrates effort to promote Big data market by funding R&D, disseminating service infrastructures, and preparing the legal system. To create new business opportunities by government driven strategy for Big data, the activities for ensuring interoperability should be continued in parallel. In this paper, we drew the potential business models by using the actors of Big data ecosystem. On this basis, we assigned the roles of government actors and, finally we drew the standardization requirements for supporting each role. The results of this study could be used for planning a road map of Big data standardization. Keywords: Big data, Big data ecosystem, Big data business model 1. Introduction Recently, the Big data paradigm caused by increase of data, connected devices, and IoT makes people focus how to save, search, process huge data and find a new business opportunities with them. Moreover, this paradigm holds a new technology to lead the IT area. The government of the US and other major countries already announced a plan to support Big data industry, standardization organizations are focusing on extending their activities to contain Big data issues. Korean government-led efforts are underway to support the domestic Big data industry. In this study, we suggest a conceptual ecosystem of Big data, and draw the potential business models based on the ecosystem. Based on them, we assigned the roles of government actors and, finally we drew the standardization requirements for supporting each role. 2. Big Data Ecosystem International standardization organizations and government agencies are trying to define a conceptual architecture or reference architecture of Big data. ITU-T SG13 Question 17 has initiated a new draft Recommendation on Big data (Y.Bigdata-reqts) [1], JTC 1 tried to identify the status of Big data standardization and potential working item by Study Group on Big Data [2, 3]. NIST reference architecture also referred to a conceptual model [4]. Each of the document uses the different terms, but the main concept are almost same. Figure 1 shows abstracted ecosystem of Big data based on the above mentioned. * Corresponding author ISSN: IJSEIA Copyright c 2014 SERSC

2 Figure 1. Ecosystem of Big Data Big data provider introduces new data or information feeds into the Big data system for discovery, access, and transformation by the Big data system. The data sources include public data from governments, organizations, Internet, private/enterprise data, and Internet based application-collected data, such as SNS, IoT data [1]. Big data service provider supplies analysis applications and infrastructures for the consumer. Analysis service activities include data visualization and analyses. Infrastructure service activities also include data collection, storage, preprocessing. The Big data consumer is the end user or other systems in order to use the results and services from data and service providers. Big data consumers could produce services or knowledge by consuming activities and furnish them to the outside of the ecosystem. 3. Potential Business Model Analysis 3.1. Business Pattern Modeling In the case of typical web services, business models are described by the value chain that consist of nodes, which are governments and private companies, individuals, etc. [6]. Moreover, the type of business model is an important criterion for selecting a commerce system, implementation technology, security and quality. The most common way for describing business model is using the relationship between the provider and the consumer (e.g., G2G, B2B). In other words, according to the type classification of the participant and the government s roles, business models could be divided into public and private type. Public type includes government-to-government (G2G), government-to-business (G2B), and government-to-citizen (G2C). In addition, private type includes business-to-business (B2B), business-to-citizen (B2C), and peer-to-peer (P2P). However, it is difficult to derive the requirements of the actor s perspective by using above classification. In this reason, we combined the method for describing business model and the abstract ecosystem proposed in chapter 2. Table 1 summarizes the association patterns among the actors in the Big data ecosystem. 166 Copyright c 2014 SERSC

3 Table 1. Association Patterns among Big data Actors Point of View Association Pattern Big data Consumer {D, {S infra, S analysis }U }U C Big data Service Provider {{S infra, S analysis }U, D {S infra, S analysis }U} C Big data Provider (Data Reproduce) {D D, D S D} In this paper, the arrow means an association that delivering data or service, {} means one of the elements of the set, and {}U means one of the elements of the whole set. For example, A B means B uses data or service provided by A. In the case of {A, B}, possible instance is A or B, and the case of the association pattern {A C, B C}U, possible instance could be one of {A C}, {B C}, {A C, B C}. Big data consumers can use Big data and/or Big data service. Big data service provider could supply individual services (analysis service or infrastructure) or data mash-up services (a case of D S C ). Big data Provider could be one of the following: supplying Big data their own reproducing Big data using the data from the different Big data Providers reproducing Big data using the data and services from other Big data Providers and Service providers Based on the association patterns described above, we can consider the actor government (G) or business (B) side. As a result, Table 2 shows the association patterns, which extended based on Table 1. Basic Pattern D C S C Table 2. Association Pattern Extensions Extended Association Pattern D(G) C(G), D(G) C(B), D(B) C(G), D(B) C(B) S infra (G) C(G), S infra (G) C(B), S infra (B) C(G), S infra (B) C(B), S analysis (G) C(G), S analysis (G) C(B), S analysis (B) C(G), S analysis (B) C(B) {D S} C {D(G) S analysis (G)} C(G) {D(G) S analysis (G)} C(B) {D(G) S analysis (B)} C(G) {D(G) S analysis (B)} C(B) {D(B) S analysis (G)} C(G) {D(B) S analysis (B)} C(G) {D(B) S analysis (B)} C(B) D D D S D D(G) D(G), D(G) D(B), D(B) D(B) D(G) S(G) D(G) D(G) {S(G), S(B)} D(G) D(G) {S(G), S(B)} D(B) 3.2. Patterns between Government Big data Provider and Consumer This is the case for mutual sharing between the public authorities of the government Big data and open it to the business actors. In other to this case, the consumer has to know following information: Copyright c 2014 SERSC 167

4 what kinds of data is/are available best practices of \ Big data for his/her work more specific information about the data such as access right, sources and history of the data; meta-meta information about data Table 3. Patterns Related Government Big Data Provider Basic Pattern D C 3.3. Government Big data Service Provider Extended Association Pattern D(G) C(G), D(G) C(B) The possible scenarios about Big data service provider are two. One is the basic pattern S C and another one is {D S} C as shown in Table 4. A government Big data service provider may provide online infrastructure systems for Big data (e.g., IaaS in cloud computing) and/or analysis services (e.g., SaaS in cloud computing). In addtiton, they could supply data mash-up services to government and business actors. In the public sector, it is possible to prevent the waste of budget due to introduce the individual Big data system, and to support Big data analysis and mash-up more easily. Moreover, business actors could get a new opportunity by using Big data and Big data service from the government even if they did not have any private Big data or service system. In this case, the government Big data service provider should support an intuitive and simplified way than high performance analysis capabilities to government users. For business consumer, analytical functions supporting various data types and analytic methods are required. Table 4. Patterns Related Government Big Data Service Provider Basic Pattern S C Extended Association Pattern S infra (G) C(G), S infra (G) C(B), S analysis (G) C(G), S analysis (G) C(B) {D S} C {D(G) S analysis (G)} C(G) {D(G) S analysis (G)} C(B) {D(B) S analysis (G)} C(G) 3.4. Government Big Data Reprocessing The Big data provider can reprocess d ata according to various purposes. It means that a Big data provider could reproduce Big data using the data from other Big data Providers and/or the analysis services from other Big data Service providers as shown in Table 5. One of the instances of the pattern D D could be a conversion of relational data into no-sql database. The case of D S D, a history of the data is the most important issue. Data lineage is generally defined as a kind of data life cycle that includes the data s origin and where it moves over time [7]. If the data had several steps of analysis, sometimes it could be distorted by the intended use. So consumers have to look at the history of data more closely and distinguish the data for the purpose. In other words, a government Big data provider has to support the functionalities for tracking and managing data lineage. 168 Copyright c 2014 SERSC

5 Table 5. Patterns Related Government Big Data Reprocessing Basic Pattern D D D S D Extended Association Pattern D(G) D(G), D(G) D(B) D(G) S analysis (G) D(G) D(G) S analysis (G) D(B) 4. Roles of Government Actors and Standardization Requirements 4.1. Government Big Data Provider The basic role of government Big data provider is opening government Big data. The case of open government data, government data shall be considered open if it is made public in a way that complies with the 8 principles (complete, primary, timely, accessible, machine processable, non-discriminatory, non-proprietary, and license-free) [8]. In addition, the Big data point of view, several technical considerations are existed. gathering the data and describing what they are supporting semantic search for consumer s ease of use user s feedback about use of the data extract and send the imperatively necessary information from sensor stream data privacy and security of data In this sense, for establishing the common registry and using it, metadata, data catalogue, taxonomies related technical standards and guidelines for security management, privacy control are required Government Big Data Service Provider A government Big data service provider supplies infrastructure services and Big data analysis services to public and business actors. An analysis service provided by government actor can contain data from other Big data provider. A number of government agencies are adopting cloud technologies and Big data infrastructure service can be served as an instance of IaaS. Although business actors could use government Big data services free in principle, limitation or rules are required to manage the quality of services. In this respect, for finding and binding services more easily, standards and guidelines for description method for service capability and workflow modeling tools, tracing the data lineage, and principles of quality control is needed Government Big data Consumer A government Big data consumer uses Big data from government and business sides, and uses Big data services from government Big data service provider. If public agency have not introduced the system for Big data, but want to use Big data, two types of approaches can be considerable. One is completely depending on data/service providers and mash-up the result of analysis with agency s own system functions. Another is binding the outside Big data at DBMS level. In the standardization perspective, guidelines for Big data analysis more easily and SQL-like language for horizontally scaling data sources. Table 5 shows summary of the standardization requirements for government-led big data. We distribute requirement of standardization into three types; conceptual model and schema, interface & implementation specification, guideline. Copyright c 2014 SERSC 169

6 Table 5. Standardization Requirements for Government Big Data Type of standardization Conceptual model and schema Interface & implementation specification Requirements Metadata for Big data Data Catalogue Taxonomy for data & usabilities Event description language Workflow description language Linage description Registry service interfaces Event Pattern Query Capability description for Big data analysis services SLA for infrastructure service SQL-like language targeted at horizontally scalable data source Guideline Security for Big data Privacy contol Guidelines for Big data analysis and mash up 5. Conclusion Big data is currently the key paradigm of IT area, and many SDO tried to support this issue. A comprehensive perspective, standardization issues should be dealt with the related business models. In this paper, we focused on potential business models of big data ecosystem. Moreover, we proposed the standardization requirements for government driven Big data by potential business scenarios. The next step of this study is reviewing each requirement and setting a priority for action plan. Acknowledgements This research was supported by the ICT Standardization program of MISP(The Ministry of Science, ICT & Future Planning). References [1] ITU-T SG 13, Draft Recommendation ITU-T Y.Bigdata-reqts (2014). [2] JTC 1 SGBD, 1 st SGBD Meeting Report, San Diego (2014). [3] JTC 1 SGBD, Final SGBD Report to JTC 1 (2014). [4] NIST Big Data PWG, Draft NIST Big Data Interoperability Framework: Volume 6, Reference Architecture (2014). [5] NIST Big Data PWG, NIST Big Data General Requirements Ver.0.2 (2013). [6] T. O Reilly, What Is Web 2.0: Design Patterns and Business Models for the Next Generation of Software (2005), no. 65, pp [7] [8] [9] S. W. Ha, J. M. Park and K. W. Nam, Application Strategies of Geospatial Service Registry for SOA in GIS Web Services, Journal of Korea Spatial Information Society, vol. 18, no. 4, (2011). [10] H. Bouwman, H. D. Vos and T. Haaker, Mobile service innovation and business models, vol. 2010, Berlin: Springer (2008). 170 Copyright c 2014 SERSC

7 Authors Dr. Suwook Ha, worked at NIA from 2002 to 2008 and has been working for Electronics and Telecommunications Research Institute (ETRI) since He has been working as a researcher in the field of ICT standardization (ISO/TC 211, TC 204, Open Geospatial Consortium, etc.) for the past 12 years. He specializes in software architecture including Geospatial Information System, Location Based Services, SNS Mining, Big Data, etc. Currently he is an expert of JTC 1 SGBD, a vicechair of NGIS PG of TTA, and a secretary of Big Data SPG of TTA. He is also working with Government to support the Next Generation Computing. Dr. Kangchan Lee has been working for ETRI since He started in Protocol Engineering Center to develop the technology and standards for Next Generation Web. Until now, he has been participated several standardization projects which are related to Web technologies, such as Ubiquitous Web Services, Mobile Web, etc, and his major research interests are Next Generation Web, Cloud Computing, Future Networks, distributed system integration, database integration technology, digital library, information retrieval and database, and structured document, etc. He has also been actively involved in international and domestic standardization activities. Regarding of international standardization activity, he has been working for a deputy manager of W3C Korea Office since Since 2005, he is working with ITU-T to develop the Webbased convergence service standard in NGN environment with several editorships in Study Group 13 of ITU-T. Also he is now the Rapporteur of NGWeb (Next Generation Web) EG (Expert Group) at ASTAP for 5 years since Dr. Seungyun Lee has been working for ETRI since He has been working as a researcher in the field of ICT standardization (IETF, W3C, ITU-T, ISO/IEC JTC 1, etc.) for the past 14 years. He specializes in software standards including the Next Generation Web including Ubiquitous Web, Social Web, Device Web, etc., Mobile Communication and Cloud Computing. Seungyun is currently a Convenor of ISO/IEC JTC 1 SC 38 WG 3 (Cloud Computing) and a Manager of W3C Korea Office. He is also a Chair of Internet Related Topic (IRT) Expert Group in Asia Pacific Tele-community Standard Program (ASTAP). Seungyun Lee has been involved several international projects including EC-IST Framework Program to develop the next generation multimedia applications and currently he is a Chair of standards technical committee at Mobile Web forum in Korea. He is also working with Government to support the national ICT strategy development in area of software technology including Cloud Computing, Web and Mobile. Copyright c 2014 SERSC 171

8 172 Copyright c 2014 SERSC

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