ORIGINAL_ARTICLE
Guest Editorial: Impact of Integrated Intelligent Information and Analytical Systems on Society
The Special Issue of the Journal of Information Technology Management (JITM) is publishing very selective papers on information management, technology in higher education, integrated systems, enterprise management, cultural thoughts, strategic contributions, management information systems, and cloud computing. We received numerous papers for this special issue but after an extensive peer-review process, 10 papers were finally selected for publication. The current special issue consisted of areas viz Integrated Intelligent Systems, Analytical Systems, and Enterprise Management.
https://jitm.ut.ac.ir/article_76287_ac70fb554bc03049037967eb3001a153.pdf
2020-09-01
1
3
10.22059/jitm.2020.76287
Cloud Technologies
Intellectual Systems
Analytical Systems
management information systems
Asadullah
Shaikh
asshaikh@nu.edu.sa
1
Associate Professor, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia.
LEAD_AUTHOR
ORIGINAL_ARTICLE
Efficiency of Information Management and Analysis for Industrial Entrepreneurship
Information Management and Analysis (IMA) implies the single data and knowledge base of the business organization whose algorithm and software complex is designed to solve functional problems of operating and dispatch management, marketing, financial arrangements, accounting, strategic planning and organizing e-document flow between the sources and users of information. The “information – communication - control” vector is directed at the achievement of corporate objectives. Assessment of IMA implementation efficiency is complicated by the fact that implementation of managerial (informational) solutions is possible indirectly, with the flows of material, financial and informational resources; the IMA algorithms are valid for a certain range of situations and modes, whose frequency and duration of action are not known in advance. The study is aimed at formalizing compatibility conditions for a set of economic and technical, actual, and probabilistic indicators applied for justifying the IMA implementation efficiency for industrial entrepreneurship. The IMA effectiveness is expected to be calculated as a total of network (communication) effects per each block of IMA algorithms, factored the presentation of information operability; to calculate cost-effectiveness (recoupment of investment) it is supposed to modernize the structural capital evaluation procedure according to A.Puliс. The article contains an introduction, concept of IMA, application of system efficiency theory to IMA, building the structure of IMA efficiency indicators, and conclusion. The main method of study: system approach.
https://jitm.ut.ac.ir/article_76288_d0217168ee0fe947a6acda4c3001a1a3.pdf
2020-09-01
4
13
10.22059/jitm.2020.76288
Information
Management Algorithms
Communication
Efficiency
Effectiveness
Cost-effectiveness
Operability
Irina
Gontareva
ivgontareva@gmail.com
1
Professor, Department of Marketing, Management, and Entrepreneurship, V.N. Karazin Kharkiv National University, Kharkiv, Ukraine.
LEAD_AUTHOR
Vitalina
Babenko
vitalinababenko@karazin.ua
2
Professor, Department of International Е-Commerce and Hotel & Restaurant Business, V.N. Karazin Kharkiv National University, Kharkiv, Ukraine.
AUTHOR
Viktoriia
Yevtushenko
v.a.evtushenko@karazin.ua
3
Head of Department of Marketing, Management and Entrepreneurship, V.N. Karazin Kharkiv National University, Kharkiv, Ukraine.
AUTHOR
Nataliia
Voloshko
voloshkonataly23@gmail.com
4
Аssociate Professor, Department of Economics and Management, Oles Honchar Dnipro National University, Dnipro, Ukraine.
AUTHOR
Yevgen
Oliynyk
psaa2011@i.ua
5
Associate Professor, Department of Management & Associate Professor of Department of Economics, Poltava State Agrarian Academy, Poltava, Ukraine.
AUTHOR
Benner, M. J., & Tushman, M. L. (2003). Exploitation, exploration, and process management: The productivity dilemma revisited. Academy of management review, 28(2), 238-256.
1
Cimoli, M., Dosi, G. & Stiglitz, J. E. (2009). Institutions and Policies Shaping Industrial Development: An Introductory Note. Industrial Policy and Development: The Political Economy of Capabilities Accumulation. New York, Oxford: Oxford Scholarship. 19-38. DOI:10.1093/acprof:oso/9780199235261.003.0002
2
Cretu, L.G. (2014). Designing enterprise architecture frameworks: Integrating business processes with IT infrastructure. Toronto: Apple Academic Press, CRC Press. 360 p.
3
Gontareva, I. V. (2011). Influence of timeliness in reproduction processes upon system efficiency of enterprise development. Actual Problems of Economics, 2(116), 69-76. (in Ukr.)
4
Hrabovskyi, Y., Babenko, V., Al’boschiy, O., Gerasimenko, V. (2020). Development of a Technology for Automation of Work with Sources of Information on the Internet. WSEAS Transactions on Business and Economics, Vol. 17, Art. #25, pp. 231-240. doi: 10.37394/23207.2020.17.25
5
Information technology and information systems. Retrieved from http://www.itstan.ru/it-i-is/klassifikacija-informacionnyh-sistem-is.html-0 [accessed January 10, 2020]
6
Jensen, M. (2010). The Modern Industrial Revolution, Exit, and the Failure of Internal Control Systems. Journal of Applied Corporate Finance. Vol. 22, No. 1, 43-58.
7
Korneyev М., Pylypenko А., Popov O., Shmatko N. (2019). Organized management of decentralized economic production systems with joint implementation of development projects. Eastern-European Journal of Enterprise Technologies, 4/3 (100), 22– 35. https://doi.org/10.15587/1729-4061.2019.175765
8
Lankhorst, V. et al. (2017). Architecture at Work: Modelling, Communication and Analysis (The Enterprise Engineering Series) 4th Edition, Kindle Edition. Springer. 360 p.
9
Malyarets, L., Draskovic, M., Babenko, V., Kochuyeva, Z. & Dorokhov, O. (2017). Theory and practice of controlling at enterprises in international business. Economic annals-XXI, 165, 90-96.
10
Murray, M. (2010). Total System Efficiency. In Power Transmission Engineering. Randall Publications LLC: Elk Grove Village, IL, Vol. 4, 1. 16-23.
11
Ponomarenko, V. & Gontareva, I. (2017). The system of causal connections between entrepreneurial activity and economic development. Economic annals-XXI. 165(5-6), 4-7. https://doi.org/10.21003/ea.V165-01
12
Pulic, A. (2000). VAIC™ an accounting tool for IC management. International Journal of Technology Management, Vol.20, No.5/6/7/8, 702-714. https://doi.org/10.1504/IJTM.2000.002891
13
Stiglitz, J. E., Sen, A. & Fitoussi, J.-P. (2009). Report by the Commission on the Measurement of Economic Performance and Social Progress: Reflections and Overview. Retrieved from https://hal-sciencespo.archives-ouvertes.fr/hal-01069384/document [accessed January 10, 2020]
14
Varney, C. (2014). Using Social Media for Internal Communications. URL: https://www.brandwatch.com/blog/internal-communications-a-look-at-the-untold-story-of-marketing.
15
ORIGINAL_ARTICLE
Technology Enhanced Internationalization in Higher Education, Non-Traditional Indicators
This paper describes an alternative view of the internationalization process in higher education by comparing the traditional and non-traditional indicators on how the level of internationalization is measured; additionally, barriers that exist on both sides are reviewed and discussed. As higher education institutions are very accustomed to and focused on the number of international students, international cooperation agreements, visiting guest professors and international projects, another dimension could be added if we start to account and measure things that are happening in the digital communication, online data exchange, usage of mobile devices and other technologies. Authors argue that it would be necessary to include this perspective in the development of internationalization strategies, institutional development plans as well as external outreach tactics. The paper is based upon empirical knowledge coming from Erasmus+ KA2 project and a brief institutional self-assessment performed by Riga Technical University International Cooperation and Foreign Students Department.
https://jitm.ut.ac.ir/article_76289_79c136e7e4f74b0b36989a78d8a1d50c.pdf
2020-09-01
14
25
10.22059/jitm.2020.76289
Internationalization
Digital Communication
Technology Indicators
International Outreach
Karlis
Valtins
karlis.valtins@rtu.lv
1
Lecturer and Researcher, Riga Technical University, ICFSD.
LEAD_AUTHOR
Igors
Tipans
igors.tipans@rtu.lv
2
Senior Researcher, Riga Technical University, ICFSD.
AUTHOR
Natalja
Muracova
natalja.muracova@rtu.lv
3
Lecturer and Researcher, Riga Technical University, ICFSD.
AUTHOR
Besselaar P., Inzelt A., Reale E., de Turckheim E., Vercesi V. (November, 2012). Indicators of internationalisation for research institutions: a new approach. ESF.
1
Brandenburg U., Federkeil G. (2007). How to Measure Internationality and Internationalisation of Higher Education Institutions! Indicators and Key Figures. Working paper No.92, Berlin.
2
Chesbrough H., (2003). “Open Innovation: The New Imperative for Creating and Profiting from Technology”, ISBN: 1-57851-837-7 published by Harvard Business School Press.
3
Cimoli, M., Dosi, G. & Stiglitz, J. E. (2009). Institutions and Policies Shaping Industrial Development: An Introductory Note. Industrial Policy and Development: The Political Economy of Capabilities Accumulation. New York, Oxford: Oxford Scholarship. 19-38. DOI:10.1093/acprof:oso/9780199235261.003.0002
4
Crăciun D. (2018). National Policies for Higher Education Internationalization: A Global Comparative Perspective. In: Curaj A., Deca L., Pricopie R. (eds) European Higher Education Area: The Impact of Past and Future Policies. Springer, Cham.
5
De Wit, H. (2017). The importance of internationalisation at home: In a time of political tensions. THEMA, 5, pp. 25-29.
6
Gao Y., Baik C., Arkoudis S. (2015). Internationalisation of Higher Education. In: Huisman J., de Boer H., Dill D.D., Souto-Otero M. (eds) The Palgrave International Handbook of Higher Education Policy and Governance. Palgrave Macmillan, London.
7
Green, M. F. (2012). Measuring and assessing internationalization, Washington, DC: NAFSA, Association of International Educators.
8
Guri-Rosenblit S. (2015). Internationalisation of Higher Education: Navigating Between Contrasting Trends. In: Curaj A., Matei L., Pricopie R., Salmi J., Scott P. (eds) The European Higher Education Area. Springer, Cham.
9
Knight J., (2014). What Is An International University? In A.Glass. Paris: OECD.
10
Measuring Success in the Internationalisation of Higher Education. (2009). Occasional Paper 22, EAIE, ISSN 0927-3514.
11
Muhammad, A., Shaikh, A., Naveed, Q. N., & Qureshi, M. R. N. (2020). Factors Affecting Academic Integrity in E-Learning of Saudi Arabian Universities. An Investigation Using Delphi and AHP. IEEE Access, 8, 16259-16268.
12
Naveed, Q. N., Qureshi, M. R. N., Tairan, N., Mohammad, A., Shaikh, A., Alsayed, A. O., ... & Alotaibi, F. M. (2020). Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making. Plos one, 15(5), e0231465.
13
Thiel P., Masters B. (2014). Zero to One: Notes on Startups, or How to Build the Future”, ISBN: 978-0804139298, published by Crown Business.
14
ORIGINAL_ARTICLE
Integrated Intelligent Information and Analytical System of Management of a Life Cycle of Products of Transport Companies
Developed an integrated intellectual computerized system of ecological-economic monitoring, modeling, and managing the life cycle of the products of technogenic enterprises of transport engineering, which is presented in the form of a 3-equation structure, functioning in conditions of instability. The proposed paradigm system life cycle management applicable to any other control system of large and complex systems, such as techno-genic type. This new paradigm of complex systems and process management, including technical systems. The system is based on the following findings: concepts, principles, a set of non-linear models, decision-making methods, and the environmental and economic governance, integral criteria. As an example, in this paper offer solution to the problem of estimating the cost of the product life cycle of railway transport.
https://jitm.ut.ac.ir/article_76291_81fc65ca9e7647cc2f978481f587e8de.pdf
2020-09-01
26
33
10.22059/jitm.2020.76291
Industry 4.0
Application Systems
Product Life Cycle Process
Technical Operation System
Continuous Acquisition System
Lifecycle Support System
Sultan
Ramazanov
sramazanov@i.ua
1
Professor, Department of Information Systems in Economics, Vadym Hetman National Economic, University of Kyiv, Kyiv, Ukraine.
AUTHOR
Vitalina
Babenko
vitalinababenko@karazin.ua
2
Professor, Department of International Е-Commerce and Hotel & Restaurant Business, V.N. Karazin Kharkiv National University, Kharkiv, Ukraine.
LEAD_AUTHOR
Oleksandr
Honcharenko
alexgontcharenko@gmail.com
3
PhD Candidate of the Institute of Information Technologies in Economics, Kyiv National Economic, University named after Vadym Hetman, Kyiv, Ukraine.
AUTHOR
Nataliia
Moisieieva
n.i.moiseeva1@gmail.com
4
Associate Professor, UNESCO Chair for «Philosophy of Human Communication», Social and Humanity Sciences, Petro Vasylenko Kharkiv National Technical University of Agriculture, Kharkiv, Ukraine.
AUTHOR
Volodymyr
Dykan
kafeoiup@ukr.net
5
Professor, Department of Economics and Management of Industrial and Commercial Business, Ukrainian State University of Railway Transport, Kharkiv, Ukraine.
AUTHOR
Babenko V., Kulczyk Z., Perevosova I., Syniavska O. and Davydova O. (2019). Factors of the development of international e-commerce under the conditions of globalization. SHS Web of Conferences, 65. pp. 10-16. DOI: https://doi.org/10.1051/shsconf/20196504016
1
Babenko, V., Sidorov, V., Koniaieva, Y., Kysliuk, L. (2019). Features in scientific and technical cooperation in the field of non-conventional renewable energy. Global Journal of Environmental Science and Management, 5 (Special issue), pp. 105-112.
2
Berg D.B (2014). lifecycle models: Proc. Benefit / D.B. Berg, E.A.Ulyanov, P.V. Kind.- Ekaterinburg: Publishing House of the Ural Mountains. University Press, 2014. - 74c.
3
Gontareva, I., Maryna, B., Babenko, V., Perevozova, I., Mokhnenko, A. (2019). Identification of efficiency factors for control over information and communication provision of sustain-able development in higher education institutions. WSEAS Trans-actions on Environment and Development. Vol. 15, рр. 593-604.
4
Hrabovskyi, Y., Babenko, V., Al’boschiy, O., Gerasimenko, V. (2020). Development of a Technology for Automation of Work with Sources of Information on the Internet. WSEAS Transactions on Business and Economics, 17, 231-240. doi: 10.37394/23207.2020.17.25
5
Lipatov S.V (2013). The architecture of the integrated AV-mated system support aircraft lifecycle // Bulletin of Samara Scientific Cen-ter of the Russian Academy of Sciences, 15 (3).
6
Pavlov N.V (2011). Methods and models of the marketing-oriented product lifecycle management, 2011. 206 p.
7
Ramazanov S.K (2008). Models and information techno ogy ecological and economic management of pro-duction system in an unstable environment. Auto-Ref. dissertations. on competition uch. step. PhD, Lugansk, 2008 - 40c.
8
Rama-zanov S.K., Nadon G.O., Krištáľ N.І, Stepanenko O.P. (2009). Іnnovatsіynі tehnologії antikrizo-Vågå up-ravlіnnya ekonomіchnimi systems: monographfіya. Timash-ova; Pid ed. prof. S.K. Ramazanov. - Luhansk - Kyiv: a kind of SNU IM. Dal, 2009. - 584 p.
9
Ships E.V (2003). Integrated information lifecycle support of engineering products. Principles. Technologies. Methods. Models. - M .: Publishing House "MBM", 2003. - 264 p.
10
Vetrov S.I, Kovshov A.N. Skvortsov A.V. (2010). A systematic approach to the design of virtual environments in production of CALS-technologies. Directory. Ing. Zh. - 2010. - N 12 (153). - S.59-64. - Bibliogr.: 9 titles.
11
Zabegalin E.V (2006). Information Architecture systems in theory and practice. IBS, Department of management consulting. 2006. URL: http://evz.name/evzms_2.pdf.
12
ORIGINAL_ARTICLE
Method of Video-Measurements of Traffic Flow Characteristics at a Road Junction
In the theory of traffic flows the main characteristics are: intensity, speed, and density. They make it possible to use hydrodynamic models. In connection with the development of modern highways and road networks, traffic flows behavior is becoming more and more complex and diverse. In particular, the B.Kerner studies have shown that the laminar solution of hydrodynamic models is poorly correlated with experimental data. Our research team is developing tools for intelligent monitoring of traffic flows on fragments of the road network with different geometries. The paper presents a project of a client-server system, which allows obtaining, in real-time, information regarding the basic characteristics of traffic flows at the junction of any configuration using mobile devices. The automation of obtaining characteristics is based on the application of image recognition algorithms (virtual detection method).
https://jitm.ut.ac.ir/article_76292_3d30ff9c2d45fc238f3cd3939ee3b2c9.pdf
2020-09-01
34
43
10.22059/jitm.2020.76292
Traffic Flow
computer vision
Traffic Model
Mobile Application
Client-server Architecture
Virtual Detection Method
Marina
Yashina
yashinamv14@gmail.com
1
Professor, Department of Higher Mathematics, Faculty of Automobile Transport, Moscow Automobile and Road Construction State Technical University (MADI), Moscow, Russia.
LEAD_AUTHOR
Maria
Belova
m_a_r_e_i@inbox.ru
2
Bachelor Student, Department of Higher Mathematics, Faculty of Automobile Transport, Moscow Automobile and Road Construction State Technical University (MADI), Moscow, Russia.
AUTHOR
Alexey
Mokhov
leshamokhov@gmail.com
3
Bachalor Student, Department of Higher Mathematics, Faculty of Automobile Transport, Moscow Automobile and Road Construction State Technical University (MADI), Moscow, Russia.
AUTHOR
Buslaev A., Yashina M., Abyshov R.& Kotovich I.. (2010).Mathematical Problems of Pattern Recognition for Traffic. in Proc. of the 2010 Seventh International Conference on Information Technology, Ed. Shahram Latifi, The IEEE Computer Society's Conference Publishing Services (CPS). p.1133 – 1135
1
Buslaev A.P., Wang N.J., Guo J.M.& Yashina M.V. (2009). On recovery of plane object shape by projections. IPCV, The 2009 World Congress in Computer Science, Computer Engineering, and Applied Computing (WORLDCOMP-09). Las Vegas, Nevada, USA (July 13-16, 2009), in Proc. of the 2009 Int.Conf. on Image processing, computer vision and pattern recognition, Eds. Hamid R. Arabnia, Gehard Schaefer, CSREA Press. p.222-226
2
Buslaev A.P., Yashina M.V.& Yashin V.B. (2010). On recovery of 3D objects by projection. WorldComp - 2010. International Conference of Image Processing, Computer Vision, and Pattern Recognition (IPCV'10), 2010, Las Vegas, USA, p. 873 - 881
3
Kozlov V.V., Buslaev A.P., Bugaev A.S., Yashina M. V., Schadschneider A.& Schreckenberg M. Preface. (2011 (2013)).In the book: Traffic and Granular Flow. DOI: 10.1007/978-3-642-39669-4
4
Lukanin V.N., Buslaev A.P., Novikov A.V.& Yashina M.V. (2003). Traffic flows modelling and evaluation of energy-ecological parameters. Part II. International Journal of Vehicle Design. 33(4) 400-421
5
Lukanin V.N., Buslaev A.P., Trofimenko Y.V.& Yashina M.V.(1998).Modelling and optimal control of transport flows in megapolis. International Journal of Vehicle Design.19(3), p. 267-281
6
Lukanin, V. N., Buslaev, A. P., Novikov, A. V.& Yashina, M. V. (2003). Traffic flows modelling and the evaluation of energy-ecological parameters. Part I. International journal of vehicle design. 33(4), p. 381-399
7
Yashina M.V.&Vinogradov A.V. (2011 (2013)). On traffic control means recognition in intelligent monitoring and traffic safety Traffic and Granular Flow. Springer. p. 439-452
8
ORIGINAL_ARTICLE
New Realities of the Enterprise Management System Information Support: Economic and Mathematical Models and Cloud Technologies
The paper focuses on the urgency of the implementation of cloud technologies, which are a necessary condition for the development of enterprise management systems, give rise to a complex of insufficiently studied phenomena and processes and determine the need to find new tools in making and implementing reasonable management decisions. In the process of research, the sequence of construction and the overall structure of the enterprise management system, based on the use of cloud technologies, are determined, which allowed to build a mathematical model for calculating the probability of making an error-free decision, evaluating the efficiency of decision-making, a model of making a management decision for a certain time with the parallel method of operation of elements of the enterprise management system.
https://jitm.ut.ac.ir/article_76293_2b4c5c48f6fcfbc80da9425d1aaea160.pdf
2020-09-01
44
60
10.22059/jitm.2020.76293
Computer technologies
management information systems
management decisions
economic and mathematical modeling
Cloud Technologies
Anatolii Asaul
Asaul
asaul@yandex.ru
1
Professor, Department of Construction Economics and Housing, St. Petersburg State University of Architecture and Civil Engineering, St. Petersburg, Russia.
AUTHOR
Mykhailo
Voynarenko
voynarenko@ukr.net
2
Professor, Department of Accounting, Audit and Taxation, Khmelnytsky National University, hmelnytsky, Ukraine.
AUTHOR
Liudmyla
Yemchuk
yemchuk777@gmail.com
3
Lecturer, Department of Accounting, Audit and Taxation, Khmelnytsky National University, Khmelnytsky, Ukraine.
AUTHOR
Larysa
Dzhulii
olimpd505@ukr.net
4
Associate Professor, Department of Accounting, Audit and Taxation, Khmelnytsky National University, Khmelnytsky, Ukraine.
LEAD_AUTHOR
Ada, L., Niculita, A., Florina, P., & Florentin, C. The Intangible Assets–A New Dimension in The Company's Success. Available at: https://doi.org/10.1016/S2212-5671(12)00156-6
1
Andrikopoulos, V., Binz, T., F. Leymann, & Strauch S. (2013) How to adapt applications for the cloud environment. Computing, 95 (6), 1–43.
2
Babenko, V., Nakisko, O., Latynin, M., Rudenko, S., Lomovskykh, L., and Girzheva, O. (2019). Procedure of Identifying of the Parameters of the Model of Management of Technological Innovations in Economic Systems, 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T), Kyiv, Ukraine, 324-328. doi: 10.1109/PICST47496.2019.9061259
3
Chervyakova, V.V., & Chervyakova, T.I., (2015) Economic aspects of the use of cloud services by domestic business entities. Bulletin of the National Transport University., 265-275.
4
Chub I.A., Novozhilova M.V., & Andronov V.A. (2017) Simulation of applied optimization problems of placement of objects with changing metric characteristics: monograph. Kharkiv: National Research Center of Ukraine,.
5
Epiphanes, A.O. (2008) i: monograph. ed. Doctor of Economics. of sciences, prof. A.O. Epiphanes. Sums: DVBS “UABS NBU”, Part 2.
6
Glushchevsky, V.,V. (2016) Adaptive mechanisms in enterprise management systems: methodology and models: monograph. V.V. Glushchevsky. Zaporizhia: KPU,.
7
Hrabovskyi, Y., Babenko, V., Al’boschiy, O., Gerasimenko, V. (2020). Development of a Technology for Automation of Work with Sources of Information on the Internet. WSEAS Transactions on Business and Economics, Vol. 17, Art. #25, pp. 231-240. doi: 10.37394/23207.2020.17.25
8
Hryhorkiv, V., Buiak, L., Verstia, A., Hryhorkiv, M.,& Savko, О. (2017) Enterprise application software implementation at the enterprise of wood processing industry: case study. International Journal of Computing. Vol. 16 (4).
9
Khajeh-Hosseini A. (2011) Decision support tools for cloud migration in the enterprise. A. Khajeh-Hosseini, I. Sommerville, J. Bogaerts, P. Teregowda. I Proceedings of the 2011 IEEE International Conference on Cloud Computing (CLOUD), IEEE, Washington, DC, USA., 541–548.
10
Kolyada, Yu.,V. (2011) An adaptive paradigm for modeling economic dynamics. Monograph. KNEU.
11
Low, C., Y. (2011) Understanding the determinants of cloud computing adoption. C. Y. Low, Y. Chen, M. C. Wu. Industrial Management& Data Systems, vol. 111, no. 7, , 1006–1023.
12
Matviychuk, A.,V. (2011) Artificial Intelligence in Economics: Neural Networks, Fuzzy Logic. Monograph: KNEU.
13
Mehta, A., D., & Madhani, P., M., (2008) Intangible Assets - An Introduction. The Accounting World, Vol. 8, No. 9, pp 11-19, Available at SSRN: https://ssrn.com/abstract=1504544
14
Naveed, Q. N., Qureshi, M. R. N. M., Shaikh, A., Alsayed, A. O., Sanober, S., & Mohiuddin, K. (2019). Evaluating and ranking cloud-based e-learning critical success factors (CSFs) using combinatorial approach. IEEE Access, 7, 157145-157157.
15
Piskunova, O.,V. (2010) Modeling of management decisions for small business development. Monograph: KNEU.
16
Sokolovskaya, Z.,M., Andrienko, V.,M., & Ivchenko, I.,Y. (2016) Mathematical and computer simulation of economic processes: monograph.; for the total. ed. ZM Sokolovskaya. Odessa: Astroprint.
17
Vitlinsky, V.,V. (2012) Risks, security, crises and sustainable development in the economy (methodologies, models, management and decision-making methods): monograph. V.V. Vitlinsky and others; ed. prof. S.K. Ramazanova. Lugansk, 70–91.
18
Vovk, V., M. (2011) Modeling of organizational processes in entrepreneurship: a monograph. V. M. Vovk, S.S. Priya, I.M. Shish. Lviv: Ivan Franko National University of Leningrad.
19
Voynarenko М., Dzhuliy V., & Yemchuk L. (2016) Development of information systems and modeling of their implementation in the business. Problems and Perspectives in Management. International Research Journal.. № 3, Vol. 14, 102-107.
20
Voynarenko, M., Dzhuliy, V., Dzhuliy, L., & Yemchuk, L. (2019) Modeling of intangible assets development and improvement processes in the enterprise management. Periodicals of Engineering and Natural Sciences. Vol. 7, No. 2, 618-628.
21
Voynarenko, M.,P. (2018) Economic Process Management Information Mechanisms: GDR Khmelnn. Report. nat. un-t; management executed by: L.V. Yemchuk (et. al.) Topic Code 8-2016; State registration number 0116U006900. Khmelnitsky.
22
Zhluktenko V.I., Begun A.V. (2005) Stochastic models in economics: Monograph: KNEU.
23
ORIGINAL_ARTICLE
Intrapreneurial Culture through Think Tanks in Higher Education Institutions
To bridge the gap between invention and innovation, prenovation has to be focused. One of the elements of prenovation is emphasizing and creating policies for institutions. This research paper highlights the importance of having an intrapreneurial culture in higher education institutions. The purpose of this paper is to review literature related to intrapreneurial culture and find out the positive aspects of having intrapreneurial culture through think tanks in higher education institutes.
https://jitm.ut.ac.ir/article_76294_7daccecb9f3a30c911c6b489a9091726.pdf
2020-09-01
61
68
10.22059/jitm.2020.76294
Intrapreneurial Culture
think tanks
Higher Education
Arabella
Bhutto
arabella.bhutto@faculty.muet.edu.pk
1
Professor, Institute of Science, Technology, and Development, Mehran University of Engineering and Technology, Jamshoro, Pakistan.
AUTHOR
Shabina
Shaikh
shabina.shaikh@isra.edu.pk
2
Assistant Professor, Department of Management Sciences, Isra University, Hyderabad, Pakistan.
LEAD_AUTHOR
Barrick, M.R., & Mount, M.K. (1991). The big five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 44(1), 1–26.
1
Bhatia, S. & Khan, P.N.U. (2013). Building an intrapreneurial culture: a Sine-Qua-non for organizations today. Global Journal of Management and Business Studies, 3(8), pp.849-854.
2
Craft, J., and Howlett, M. (2012). Policy formulation, governance shifts and policy influence: Location and content in policy advisory systems. Journal of Public Policy, 32(02), 79–98.
3
Eickelpasch, A., Fritsch, M. (2005). Contests for cooperation - A new approach in German innovation policy. Res. Policy 34, 1269–1282. doi:10.1016/j.respol.2005.02.009.
4
Envick, B.R., & Langford, M. (2000). The five-factor model of personality: Assessing entrepreneurs and managers. Academy of Entrepreneurship Journal, 6(1), 6–17.
5
Frank, A., Krempkow, R., Mostovova, E. (2017). Gründungsradar 2016. Essen.
6
Fraussen, B. & Halpin, D. (2017). Think tanks and strategic policy-making: the contribution of think tanks to policy advisory systems. Policy Sciences, 50(1), pp.105-124. DOI: 10.1007/s11077-016-9246-0
7
Fraussen, B., Pattyn, V., & Laware´e, J. (2016). Thinking in splendid isolation? The organization and policy engagement of think tanks in Belgium. In M. Brans & D. Aubin (Eds.), Policy analysis in Belgium. Bristol: Policy Press.
8
Garcίa-Cabrera, A.M., & Garcίa-Soto, M.G. (2009). A dynamic model of technology-based opportunity recognition. Journal of Entrepreneurship, 18(2), 167–190.
9
Goosen, C. J., de Coning, T. J., & Smit, E. M. (2002). Corporate entrepreneurship and financial performance: The role of management. South African Journal of Business Management, 33(4), 21-27. Retrieved December 05, 2006, from Business Source Elite database
10
Kauffman Foundation, (2008). Entrepreneurship in American Higher Education. Kansas City.
11
Pinchot, G. (1985). Intrapreneuring: Why You Don’t Have to Leave the Corporation to Become an Entrepreneur. New York: Harper and Row
12
Rahman, O. A. (2013). The Essentials of Science, Technology and Innovation Policy. ISBN: 978-983-9445-95-4
13
Rasmussen, E., Borch, O.J. (2010). University capabilities in facilitating entrepreneurship: A longitudinal study of spin-off ventures at mid-range universities. Res. Policy 39, 602–612. doi:10.1016/j.respol.2010.02.002
14
Rich, A. (2004). Think tanks, public policy, and the politics of expertise. Cambridge: Cambridge University Press
15
Roe, R.A., & Ester, P. (1999). Values and work: Empirical findings and theoretical perspective. Applied Psychology: An International Review, 48(1), 1–21
16
Sherrington, P. (2000). British think tanks: advancing the intellectual debate?. The British Journal of Politics and International Relations, 2(2), pp.256-263. DOI: 10.1111/1467-856X.00036
17
Sinha, N. & Srivastava, K.B. (2013). Association of personality, work values and socio-cultural factors with intrapreneurial orientation. The Journal of Entrepreneurship, 22(1), pp.97-113. DOI: 10.1177/0971355712469186
18
Sohail, M.S. & Daud, S. (2009). Knowledge sharing in higher education institutions. Vine. DOI: 10.1108/03055720910988841
19
Teltumbde, A. (2006). ‘Entrepreneurs & Intrapreneurs in Corporations’, Vikalpa, Vol 31, No 1.
20
Toftoy, C. & Chatterjee, J. (2004). The intrapreneurial revolution: now is the time for action. Retrived from http://www. sbaer. uca. edu/research/icsb/2005/192. pdf.
21
Velazquez, L., Munguia, N.P.A. & Taddei, J. (2004). A Sustainable University: What Can the Matter be?, Environmental Management Sustainable Universities, Monterrey.
22
Walter, S.G., Parboteeah, K.P., and Walter, A. (2013). University departments and self-employment intentions of business students: A cross-level analysis. Entrepreneurship Theory Practice. 37, 175–200. doi:10.1111/j.1540-6520.2011.00460.x
23
Weiner, Y., & Vardi, Y. (1980). Relationships between jobs, organization, and career commitments and work outcomes - An integrative approach. Organizational Behaviour and Human Performance, 26(1), 81–96.
24
ORIGINAL_ARTICLE
Strategic Guidelines for the Improvement of Logistic Activities of Trade Enterprises
Logistics has long been recognized as the main effective tool for generating competitive advantages in trading enterprises, and therefore there is an acute problem in finding strategic guidelines for improving logistics activities through the lens of organizational and economic support of logistic activity of a trading enterprise. The article compares the main reference models for the analysis of logistics activities and found the most appropriate for use - SCOR model, which identified the main indicators for evaluating the performance of logistics activities. An algorithm for determining the level of efficiency and performance of logistic activity have been constructed. According to the results of the study, the level of organizational and economic support, perormance and efficiency of logistic activity of trade enterprises of Ukraine of the sample population have been determined.
https://jitm.ut.ac.ir/article_76295_c74714a0a193323a8f031ca7182b527c.pdf
2020-09-01
69
81
10.22059/jitm.2020.76295
Trading Company
Logistics
Organizational and Economic Support
Reference Model
SCOR Model
Tatyana
Valerievna Shtal
shtaltv@gmail.com
1
Professor, Dean of International Economic Relations Faculty, Simon Kuznets Kharkiv National University of Economic, Kharkiv, Ukraine.
LEAD_AUTHOR
Anastasiya
Ievgenievna Uvarova
anastasiya.uvarova@hneu.net
2
Postraduate Student, Department of International Economics and Management of Foreign Economic Activity, Simon Kuznets Kharkiv National University of Economic, Kharkiv, Ukraine.
AUTHOR
Nadiia
Viktorivna Proskurnina
nadiyaproskurnina@gmail.com
3
Associate Professor, Head of International Economics and Management of Foreign Economic Activity Department, Simon Kuznets Kharkiv National University of Economic, Kharkiv, Ukraine.
AUTHOR
Nataliia
Leonidivna Savytska
natalisavitska2010@gmail.com
4
Professor, Head of Marketing and Commercial Activities Department, Kharkiv State University of Food Technology and Trade, Kharkiv, Ukraine.
AUTHOR
Chornopiska N.V (2008). Methodical approaches of estimation of logistic activity of the enterprise [Electronic resource]. Access mode: http://vlp.com.ua/files/38_4.pdf.
1
Christopher M (2004). Logistics and Supply Chain Management. St. Petersburg: Peter, 2004, 316 p.
2
Frolova L.V (2005). The mechanism of logistic management of a trading enterprise. Donetsk: DonNUET, 2005, 322 p.
3
Ilchenko N.B (2016). Logistic strategies in trade. Kiev: Kiev. nat. trading econ. Univ., 2016, 432 p.
4
Ilchenko N.B (2017). Reference models of supply chain management in trade enterprises. Goods and markets, 2017, No. 2 (2), 62–71.
5
Kochubey D.V (2009). Estimation of efficiency of functioning of logistic system of trading enterprises. Bulletin of KNTEU, 2009, № 4. 59-67.
6
Kolodizieva T.O (2016). Supply Chain Management. Kharkov: KhNEU them. S. Kuznets, 2016. 164 p.
7
Krykavsky E.V., Pokhilchenko O.A (2012). Evaluation of the Performance of Innovative Supply Chains. [Electronic resource]. Access mode: http://ena.lp.edu.ua:8080/bitstream/ntb/23696/1/15-87-95.pdf.
8
Krykavskyy E.V (2005). Logistics management. Lviv: Lviv Polytechnic National University, 2005, 684 p.
9
Merzlyak A.V (2015). The role of information and strategy in supply chain management models. Russian Entrepreneurship, 16(22), 4099–4118. doi: 10.18334/rp.16.22.2115.
10
Ramazanov, S., Antoshkina, L., Babenko, V., & Akhmedov, R. (2019). Integrated model of stochastic dynamics for control of a socio-ecological-oriented innovation economy. Periodicals of Engineering and Natural Sciences, 7(2), 763-773. doi: http://dx.doi.org/10.21533/pen.v7i2.557
11
Saeed, S., Shaikh, A., Memon, M. A., Memon, M. H., Abassi, F. A., & Naqvi, S. M. R. (2017). Implementation of failure enterprise systems in organizational perspective framework. International journal of advanced computer science and applications, 8(5), 54-63.
12
Uvarova A.E (2019). Methodical bases of the evaluation of organizational and economic support of trade enterprise logistic activity. Bulletin of Chernivtsi Trade and Economic Institute. Economic Sciences, 2019, Issue I (73), 113-122.
13
Waters D (2003). Logistics. Supply Chain Management. M.: UNITI-DANA, 2003, 503 p.
14
ORIGINAL_ARTICLE
Strategic Contribution of a Business Process to Company’s Performance
The study is aimed at assessing the strategic importance of a business processes for achieving sustainable competitive advantage, therefore, in this article the theoretical approach for measuring the strategic contribution of a business process to an enterprise’s business system is presented. For evaluating of a business process strategic importance the study proposes the system of economic and managerial indicators, which includes the process’ contribution to the added value, its compliance to critical success factors, and its organizational involvement. Сombining these three indicators into one integral allows it to be used in different types of matrix analysis to make decisions on improving of a company’s business system.
https://jitm.ut.ac.ir/article_76296_bff9dffe0c0e7bacc739e2ad65b32535.pdf
2020-09-01
82
99
10.22059/jitm.2020.76296
: Business Processes’ Significance
Strategic Contribution, Organizational Involvement, Added Value, Critical Success Factors, Assets
Pavlo
Brin
pavelbrin@ukr.net
1
Professor, Department of Management and Taxation, National Technical University “Kharkiv Polytechnic Institute”, Kharkiv, Ukraine.
LEAD_AUTHOR
Olena
Prokhorenko
a_prokhorenko@meta.ua
2
Associate Professor, Department of Management and Taxation, National Technical University, Kharkiv Polytechnic Institute, Kharkiv, Ukraine.
AUTHOR
Mohamad
Nehme
mohammadnehme@gmail.com
3
PhD Candidate, Department of Management and Taxation, National Technical University, Kharkiv Polytechnic Institute, Kharkiv, Ukraine.
AUTHOR
Hussein
Trabulsi
husseintrabulsi@yahoo.fr
4
Associate Professor, Department of Economics, Lebanese University, Beirut, Lebanon.
AUTHOR
Ariyachandra, T. R., & Frolick, M. N. (2008). Critical success factors in business performance management − Striving for success. Information systems management, 25(2), 113-120
1
Babenko, V., Lomovskykh, L., Oriekhova, A., Korchynska, L., Krutko, M., Koniaieva, Y.: Features of methods and models in risk management of IT projects, Periodicals of Engineering and Natural Sciences, 7(2), 629-636 (2019). doi: http://dx.doi.org/10.21533/pen.v7i2.558
2
Benner, M. J., & Tushman, M. L. (2003). Exploitation, exploration, and process management: The productivity dilemma revisited. Academy of management review, 28(2), 238-256
3
Bolsinger, M., Bewernik, M. A., & Buhl, H. U. (2011). Value-based process improvement. In Proceedings of the European Conference on Information Systems (p. 21). Finnland, Helsinki.
4
Brin, P. & Prokhorenko, O. (2014) Quantifying the integration degree of an enterprise. Actual Problems of Economics 7 (157), 484-494.
5
Buhl, H. U., Röglinger, M., Stöckl, S., & Braunwarth, K. S. (2011). Value orientation in process management. Business & Information Systems Engineering, 3(3), 163. Retrieved from https://doi.org/10.1007/s12599-011-0157-5 [accessed January 13, 2020]
6
Curtis, B., & Alden, J. (2006). BPM & organizational maturity. Business Process Trends Column, November, (pp.1–5). Retrieved from www.bptrends.com/publicationfiles/02-07-COL-BPMMWhatWhyHow-CurtisAlden-Final.pdf [accessed January 13, 2020]
7
Dumas, M., La Rosa, M., Mendling, J., & Reijers, H. A. (2013). Fundamentals of business process management. Germany, Berlin, Heidelberg: Springer.
8
Dunn, S. (2009). Maintenance Outsourcing - Critical Issues. [Online]. Retrieved from http://www. https://www.plant-maintenance.com/outsourcing_crit_issues.shtml [accessed January 13, 2020]
9
Fisher, D. M. (2004). The business process maturity model: a practical approach for identifying opportunities for optimization. Business Process Trends, 9(4), 11-15.
10
Gontareva, I., Maryna, B., Babenko, V., Perevozova, I., Mokhnenko, A. (2019). Identification of efficiency factors for control over information and communication provision of sustainable development in higher education institutions. WSEAS Transactions on Environment and Development. Vol. 15, рр. 593-604
11
Havur, G., Cabanillas, C., Mendling, J., & Polleres, A. (2016). Resource allocation with dependencies in business process management systems. In: La Rosa, M., Loos, P., Pastor, O. (eds.), International Conference on Business Process Management vol. 260, (pp. 3-19). Springer, Cham.
12
Kataev, M., Bulysheva, L., Emelyanenko, A., & Bi, Z. (2016). Enterprise diagnostics for evaluation of enterprise business processes. Journal of Industrial Integration and Management, 1(02), 1650008. Retrieved from https://doi.org/10.1142/S2424862216500081
13
Malyarets, L.M., Babenko, V.O., Nazarenko, O.V., Ryzhikova, N.I.: The Modeling of Multi-criteria Assessment Activity in Enterprise Management, Int. J Sup. Chain. Mgt, vol. 8, no. 4, pp. 997-1004 (2019).Masood, S. A., Jahanzaib, M., & Akhtar, K. (2013). Key performance indicators prioritization in whole business process: A Case of Manufacturing Industry. Life Science Journal, 10(4), 195-201
14
Memon, F. A., Saeed, S., & Shaikh, A. (2018). Systematic Approach of Customer Relationship Management in Much Different Organization. IBT JOURNAL OF BUSINESS STUDIES (JBS), 14(2).
15
Modrák, V. (2004). Evaluation of Structural Properties for Business Processes. In Sixt International Conference of Enterprise Information Systems: ICEIS, (pp. 619-622). Portugal. Porto. Proceedings, Universidade Portucalense.
16
Nadarajah, D., & Syed A. Kadir, S. L. (2016). Measuring Business Process Management using business process orientation and process improvement initiatives. Business process management journal, 22(6), 1069-1078.
17
Ohlsson, J., Han, S., & Bouwman, H. (2017). The prioritization and categorization method (PCM) process evaluation at Ericsson: a case study. Business Process Management Journal, 23(2), 377-398.
18
Petro, Y., & Gardiner, P. (2015). An investigation of the influence of organizational design on project portfolio success, effectiveness and business efficiency for project-based organizations. International Journal of Project Management,33(8), 1717-1729.
19
Polančič G., Brin P., Kuhar S., Jošt G., & Huber J. (2019) An Empirical Investigation of the Cultural Impacts on the Business Process Concepts’ Representations. Lecture Notes in Business Information Processing, 361, 296-311.
20
Porter, M. (1990). The competitiveness of nations. Harvard Business Review, 73-93.
21
Robson, M., & Ullah, P. (1996). A practical guide to business process re-engineering. England, Hampshire: Gower Publishing, Ltd.
22
Rosemann, M., & vom Brocke, J. (2015). The six core elements of business process management. In Handbook on business process management 1 (pp. 105-122). Germany, Berlin, Heidelberg, Springer.
23
Trkman, P. (2010). The critical success factors of business process management. International journal of information management, 30(2), 125-134.
24
Van Looy, A., & Shafagatova, A. (2016). Business process performance measurement: a structured literature review of indicators, measures and metrics. SpringerPlus, 5(1), 1797.
25
Van Looy, A., Poels, G., & Snoeck, M. (2017). Evaluating business process maturity models. Journal of the Association for Information Systems, 18(6), 461-486.
26
vom Brocke, J., & Sonnenberg, C. (2015). Value-orientation in business process management. In Handbook on Business Process Management 2 (pp. 101-132). Germany, Berlin, Heidelberg, Springer.
27
vom Brocke, J., Zelt, S., & Schmiedel, T. (2016). On the role of context in business process management. International Journal of Information Management, 36(3), 486-495.
28
Weske, M. (2012). Business process management architectures. In Business Process Management (pp. 333-371). Germany, Berlin, Heidelberg, Springer.
29
Zahalni zasady otsinky maina i mainovykh prav [General Principles of Appraisal of Property and Property Rights] (2003) (National standard № 1440) (UA) Retrieved from http://zakon4.rada.gov.ua/laws/show/1440-2003-п [accessed January 13, 2020]
30
Zamecnik, R., & Rajnoha, R. (2015). Business process performance measurement under conditions o f business practice. Procedia Economics and Finance, 26, 742-749.
31
ORIGINAL_ARTICLE
Cloud Computing Technology Algorithms Capabilities in Managing and Processing Big Data in Business Organizations: MapReduce, Hadoop, Parallel Programming
The objective of this study is to verify the importance of the capabilities of cloud computing services in managing and analyzing big data in business organizations because the rapid development in the use of information technology in general and network technology in particular, has led to the trend of many organizations to make their applications available for use via electronic platforms hosted by various Companies on their servers or so-called cloud computing that have become an excellent opportunity to provide services efficiently and at low cost, but managing big data presents a definite challenge in the cloud space beginning with the processes of extracting, processing data, storing data and analyze it. Through this study, we dealt with the concept of cloud computing and its capabilities in business organizations. We also interpreted the notion of big data and its distinct characteristics and sources. Finally, the relationship between cloud computing with big data was also explained (extraction, storage, analysis).
https://jitm.ut.ac.ir/article_76298_a80e2f2d8e2a2139edcde8faf076e5ff.pdf
2020-09-01
100
113
10.22059/jitm.2020.76298
Cloud Computing
data
Data Warehouse
Big data
artificial intelligence (AI)
Business Organizations
Munsif
Sokiyna
munsif.sokiyna@gmail.com
1
PhD Candidate, Department of Management Information System, Cyprus International University, Cyprus/Nicosia.
LEAD_AUTHOR
Musbah
J. Aqel
maqel@ciu.edu.tr
2
Assistant Professor, Department of Management Information System, Cyprus International University, Cyprus/Nicosia.
AUTHOR
Omar A.
Naqshbandi
eng.omar@hmu.edu.iq
3
PhD Candidate, Department of Management Information System, Cyprus International University, Cyprus/Nicosia.
AUTHOR
Alvaro, P., Condie, T., Conway, N., Elmeleegy, K., Hellerstein, J. M., & Sears, R. (2010). Boom analytics: exploring data-centric, declarative programming for the cloud. In Proceedings of the 5th European conference on Computer systems (pp. 223–236).
1
Alyass, A., Turcotte, M., & Meyre, D. (2015). From big data analysis to personalized medicine for all: challenges and opportunities. BMC Medical Genomics, 8(1), 33.
2
Bagheri, H., & Shaltooki, A. A. (2015). Big Data: challenges, opportunities and Cloud based solutions. International Journal of Electrical and Computer Engineering, 5(2), 340.
3
Bell, M. W. (2008). Toward a definition of “virtual worlds.” Journal For Virtual Worlds Research, 1(1).
4
BOOTH, C. (2019a). Number of people using social media platforms. Retrieved from https://www.statista.com/topics/1164/social-networks/
5
BOOTH, C. (2019b). The most popular social media networks each year, gloriously animated. Retrieved from https://thenextweb.com/tech/2019/06/11/most-popular-social-media-networks-year-animated
6
Dasgupta, A. (2013). Big data: The future is in analytics. Geospatial World, 3(9), 28–36.
7
Dobre, C., & Xhafa, F. (2014). Parallel programming paradigms and frameworks in big data era. International Journal of Parallel Programming, 42(5), 710–738.
8
Erl, T., & Khattak, W. (n.d.). i Buhler, P.(2016). Big Data Fundamentals: Concepts, Drivers & Techniques. Prentice Hall.
9
Experiment, T. Dz. (2011). The DØ Experiment . Retrieved from https://www-d0.fnal.gov
10
Gewirtz, D. (2018). Volume, velocity, and variety: Understanding the three V’s of big data | ZDNet. Retrieved from https://www.zdnet.com/article/volume-velocity-and-variety-understanding-the-three-vs-of-big-data/
11
Gomes, P. (2016). Log analysis | Loggly. Retrieved from https://www.loggly.com/product/log-analysis/
12
Gu, R., Yang, X., Yan, J., Sun, Y., Wang, B., Yuan, C., & Huang, Y. (2014). SHadoop: Improving MapReduce performance by optimizing job execution mechanism in Hadoop clusters. Journal of Parallel and Distributed Computing, 74(3), 2166–2179.
13
Hadoopa, A. (2019). Apache Hadoop. Retrieved from http://hadoop.apache.org//
14
Hammer, C., Kostroch, M. D. C., & Quiros, M. G. (2017). Big Data: Potential, Challenges and Statistical Implications. International Monetary Fund.
15
IMMERMAN, G. (2017). What is big data velocity? Retrieved from https://www.machinemetrics.com/blog/what-is-big-data-velocity
16
Jain, A. (2016). The 5 V’s of big data - Watson Health Perspectives. Retrieved from https://www.ibm.com/blogs/watson-health/the-5-vs-of-big-data/
17
Ji, C., Li, Y., Qiu, W., Awada, U., & Li, K. (2012). Big data processing in cloud computing environments. Proceedings of the 2012 International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2012, 17–23. https://doi.org/10.1109/I-SPAN.2012.9
18
Kalil, T. (2012). Big Data is a Big Deal | whitehouse.gov. Retrieved January 30, 2020, from https://obamawhitehouse.archives.gov/blog/2012/03/29/big-data-big-deal
19
Khan, S., Shakil, K. A., & Alam, M. (2018). Cloud-based big data analytics—a survey of current research and future directions. In Big Data Analytics (pp. 595–604). Springer.
20
Kobielus, J. G. (2012). The Forrester WaveTM: Enterprise Hadoop Solutions, Q1 2012. Forrester Research.
21
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition.
22
Mao, R., Xu, H., Wu, W., Li, J., Li, Y., & Lu, M. (2015). Overcoming the challenge of variety: big data abstraction, the next evolution of data management for AAL communication systems. IEEE Communications Magazine, 53(1), 42–47.
23
Marr, B. (2015). Big Data: Using SMART big data, analytics and metrics to make better decisions and improve performance. John Wiley & Sons.
24
Matthews, K. (2018). Here’s How Much Big Data Companies Make On The Internet - Big Data Showcase. Retrieved from https://bigdatashowcase.com/how-much-big-data-companies-make-on-internet/
25
Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
26
Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. Special Publication 800-145. Gaithersburg: National Institute of Standards and Technology.
27
Naimi, A. I., & Westreich, D. J. (2014). Big data: A revolution that will transform how we live, work, and think. Oxford University Press.
28
Noraziah, A., Fakherldin, M. A. I., Adam, K., & Majid, M. A. (2017). Big Data Processing in Cloud Computing Environments. Advanced Science Letters, 23(11), 11092–11095.
29
Pettey, C., & van der Meulen, R. (2012). Gartner’s 2012 Hype cycle for emerging technologies identifies" Tipping point" technologies that will unlock long-awaited technology scenarios. Hype Cycle Special Report. P1-4.
30
Pickell, D. (2018). Structured vs Unstructured Data – What’s the Difference? Retrieved from https://learn.g2.com/structured-vs-unstructured-data
31
Purcell, B. M. (2014). Big data using cloud computing. Journal of Technology Research, 5, 1.
32
Ramapriyan, H. K. (2015). The Role and Evolution of NASA’s Earth Science Data Systems.
33
Slagter, K., Hsu, C.-H., & Chung, Y.-C. (2015). An adaptive and memory efficient sampling mechanism for partitioning in MapReduce. International Journal of Parallel Programming, 43(3), 489–507.
34
Sykuta, M. E. (2016). Big data in agriculture: property rights, privacy and competition in ag data services. International Food and Agribusiness Management Review, 19(1030-2016–83141), 57–74.
35
Voruganti, S. (2014). Map Reduce a Programming Model for Cloud Computing Based On Hadoop Ecosystem. International Journal of Computer Science and Information Technologies, 5(3).
36
Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13–53. https://doi.org/10.1080/17538947.2016.1239771
37
Yangjun, C. (2014). Semistructured-Data Model Sept. 2014Yangjun Chen ACS Semistructured-Data Model Semistructured data XML Document type definitions XML schema. - ppt download. Retrieved from https://slideplayer.com/slide/4950204/
38
Zhou, X., Lu, J., Li, C., & Du, X. (2012). Big data challenge in the management perspective. Communications of the CCF, 8, 16–20.
39
Zikopoulos, P., Eaton, C., & others. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media.
40