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Journal articles on the topic 'Operational Intelligence'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

PĂSAT-MIC, Adriana Anamaria, and Lucian Alexandru VASILE. "INTELLIGENCE PREPARATION OF THE OPERATIONAL AREA." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 20 (June 18, 2018): 67–72. http://dx.doi.org/10.19062/2247-3173.2018.20.7.

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2

Cline, Lawrence E. "Operational Intelligence in Peace Enforcement and Stability Operations." International Journal of Intelligence and CounterIntelligence 15, no. 2 (April 2002): 179–94. http://dx.doi.org/10.1080/08850600252869010.

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3

Glantz, David M. "Soviet operational intelligence in the Kursk operation, July 1943." Intelligence and National Security 5, no. 1 (January 1990): 5–49. http://dx.doi.org/10.1080/02684529008432034.

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4

Pidd, M. "Artificial Intelligence in Operational Research." European Journal of Information Systems 3, no. 3 (July 1994): 243–44. http://dx.doi.org/10.1057/ejis.1994.27.

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5

Dayal, Umeshwar, Harumi Kuno, Janet L. Wiener, Kevin Wilkinson, Archana Ganapathi, and Stefan Krompass. "Managing operational business intelligence workloads." ACM SIGOPS Operating Systems Review 43, no. 1 (January 2009): 92–98. http://dx.doi.org/10.1145/1496909.1496927.

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6

Mattern, Troy, John Felker, Randy Borum, and George Bamford. "Operational Levels of Cyber Intelligence." International Journal of Intelligence and CounterIntelligence 27, no. 4 (August 6, 2014): 702–19. http://dx.doi.org/10.1080/08850607.2014.924811.

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7

Reese, John R. "A case study in operational intelligence." International Journal of Intelligence and CounterIntelligence 11, no. 1 (March 1998): 73–92. http://dx.doi.org/10.1080/08850609808435365.

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8

Seibold, Michael, Dean Jacobs, and Alfons Kemper. "Operational Business Intelligence: Processing Mixed Workloads." IT Professional 15, no. 5 (September 2013): 16–21. http://dx.doi.org/10.1109/mitp.2012.68.

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9

CRISTEA, Dragos Sebastian, Liliana Mihaela MOGA, Mihaela NECULITA, Olegas PRENTKOVSKIS, Khalil MD NOR, and Abbas MARDANI. "OPERATIONAL SHIPPING INTELLIGENCE THROUGH DISTRIBUTED CLOUD COMPUTING." Journal of Business Economics and Management 18, no. 4 (August 27, 2017): 695–725. http://dx.doi.org/10.3846/16111699.2017.1329162.

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This paper provides a conceptual architecture for a cloud based platform design, that implements continuously data storage and analysis services for large maritime ships, with the purpose to provide valuable insights for maritime transportation business. We do this by first identifying the need on the shipping market for such kind of systems and also the significance and impact of different factors related to shipping business processes. The architecture presented throughout this paper will be defined around some of the most currently used ICT technologies, like Amazon Cloud Services, Sql Server Databases, .NET Platform, Matlab 2016 or javascript visualization libraries. The proposed system makes possible for a maritime company to gain more knowledge for optimizing the efficiency of its operations, to increase its financial benefits and its competitive advantage. The platform architecture was designed to make possible the storage and manipulation of very large datasets, also allowing the possibility of using different data mining techniques for inferring knowledge or to validate already existent models. Ultimately, the developed methodology and the presented outcomes demonstrate a vast potential of creating better technological management systems for the shipping industry, starting from the challenges but also from the huge opportunities this sector can offer.

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10

Kaula, Rajeev. "Operational Intelligence Performance Inferencing Across Business Processes." International Journal of Database Management Systems 12, no. 1 (February 29, 2020): 1–21. http://dx.doi.org/10.5121/ijdms.2020.12101.

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11

Briggs, Chad M. "Developing Strategic and Operational Environmental Intelligence Capabilities." Intelligence and National Security 27, no. 5 (October 2012): 653–68. http://dx.doi.org/10.1080/02684527.2012.708519.

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12

Bascur, O. A., and A. Soudek. "Grinding and Flotation Optimization Using Operational Intelligence." Mining, Metallurgy & Exploration 36, no. 1 (January 11, 2019): 139–49. http://dx.doi.org/10.1007/s42461-018-0036-4.

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13

Yermish, Ira, Virginia Miori, John Yi, Rashmi Malhotra, and Ronald Klimberg. "Business Plus Intelligence Plus Technology Equals Business Intelligence." International Journal of Business Intelligence Research 1, no. 1 (January 2010): 48–63. http://dx.doi.org/10.4018/jbir.2010071704.

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In this article the authors will show how the parallel developments of information technology at the operational business level and decision support concepts progressed through the decades of the twentieth century with only minimal success at strategic application. They will posit that the twin technological developments of the world-wide-web and very inexpensive mass storage provided the environment to facilitate the convergence of business operations and decision support into the strategic application of business intelligence.

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14

Kiranyaz, Serkan, Turker Ince, Alexandros Iosifidis, and Moncef Gabbouj. "Operational neural networks." Neural Computing and Applications 32, no. 11 (March 6, 2020): 6645–68. http://dx.doi.org/10.1007/s00521-020-04780-3.

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15

Serdyukov, V. I., N. A. Serdyukova, and S. I. Shishkina. "Improving Operational Reliability by Means of Artificial Intelligence." Russian Engineering Research 38, no. 1 (January 2018): 15–18. http://dx.doi.org/10.3103/s1068798x1801015x.

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16

Ilardi, Gaetano Joe. "IRA operational intelligence: the heartbeat of the war." Small Wars & Insurgencies 21, no. 2 (June 2010): 331–58. http://dx.doi.org/10.1080/09592318.2010.481429.

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17

Kaula, Rajeev. "Operational intelligence through performance trends: an oracle prototype." International Journal of Business Intelligence and Systems Engineering 1, no. 2 (2017): 140. http://dx.doi.org/10.1504/ijbise.2017.088686.

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Kaula, Rajeev. "Operational intelligence through performance trends: an oracle prototype." International Journal of Business Intelligence and Systems Engineering 1, no. 2 (2017): 140. http://dx.doi.org/10.1504/ijbise.2017.10009648.

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19

Al-witwit, Saif Salam Ibrahim, and Abdullahi Abdu Ibrahim. "Improving Operational Efficiency of Government using Artificial Intelligence." IOP Conference Series: Materials Science and Engineering 928 (November 19, 2020): 022014. http://dx.doi.org/10.1088/1757-899x/928/2/022014.

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20

Azvine, B., Z. Cui, B. Majeed, and M. Spott. "Operational risk management with real-time business intelligence." BT Technology Journal 25, no. 1 (January 2007): 154–67. http://dx.doi.org/10.1007/s10550-007-0017-5.

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21

Di Girolamo, Alessandro, Federica Legger, Panos Paparrigopoulos, Alexei Klimentov, Jaroslava Schovancová, Valentin Kuznetsov, Mario Lassnig, et al. "Operational Intelligence for Distributed Computing Systems for Exascale Science." EPJ Web of Conferences 245 (2020): 03017. http://dx.doi.org/10.1051/epjconf/202024503017.

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In the near future, large scientific collaborations will face unprecedented computing challenges. Processing and storing exabyte datasets require a federated infrastructure of distributed computing resources. The current systems have proven to be mature and capable of meeting the experiment goals, by allowing timely delivery of scientific results. However, a substantial amount of interventions from software developers, shifters and operational teams is needed to efficiently manage such heterogeneous infrastructures. A wealth of operational data can be exploited to increase the level of automation in computing operations by using adequate techniques, such as machine learning (ML), tailored to solve specific problems. The Operational Intelligence project is a joint effort from various WLCG communities aimed at increasing the level of automation in computing operations. We discuss how state-of-the-art technologies can be used to build general solutions to common problems and to reduce the operational cost of the experiment computing infrastructure.

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22

Melikhov, Alexander Ivanovich, Gennady Svyatoslavovich Pratsko, Victoria Aleksandrovna Chistova, Olga Dmitrievna Tyutyunik, and Olga Aleksandrovna Nenakhova. "Operational and Intelligence Activities of the Law Enforcement Agencies in the System of Ensuring National Security." SHS Web of Conferences 108 (2021): 03017. http://dx.doi.org/10.1051/shsconf/202110803017.

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The transition to the postmodern stage of development of Russian society, by the subsequent change of the system of civilizational values, required the scientific development of a new attitude to security as a basic human need with regard to changing the nature of traditional threats and interests and the emergence of completely new ones. The process of globalization being developed in the information age weakens traditional state institutions and requires a new look at national security not only as a category of foreign policy and military matters but also as an internal problem solved through operational and intelligence activities. The purpose of the study was to identify the current problems in the theory of national security by means of an analysis of scientific studies of the phenomenon of security in Russia; to consider security as a function and feature of the social system; to consider the Operational and intelligence activities of the law enforcement agencies as a mean for ensuring national security; to identify the conditions and factors of operational and intelligence activities that negatively affect the effectiveness of ensuring national security. In the course of the research, using computer indexing, about 1300 scientific, educational and methodological sources on national security and internal affairs issues have been processed and analyzed in the semantic, philosophical and legal aspects. The study examines modern theoretical and practical problems of ensuring national security as part of operational and intelligence activities of the law enforcement agencies. Operational and intelligence activities is considered in the national security system as a means of its information support, as well as as a tool for combating criminal, military and other threats. For the first time, considered are the conditions and factors of the operational and intelligence activities that negatively affect the effectiveness of ensuring national security.

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23

Kaula, Rajeev. "Performance trends for operational intelligence through value chain model." Journal of Advanced Computer Science & Technology 5, no. 2 (June 28, 2016): 34. http://dx.doi.org/10.14419/jacst.v5i2.5861.

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Value chain is a successful management model for improving business competitiveness. A value chain based analytic approach facilitates meaningful grouping of business processes such that appropriate value from business operations can be derived. As organizations focus on competitive advantage and growth, a value chain derived operational intelligence provides insight to compete successfully in the marketplace. This paper outlines an approach to develop performance based value metrics in the form of analytic business rules for operational intelligence through the value chain model. The paper illustrates the concepts through a University value chain prototype which is implemented in Oracle’s PL/SQL language.

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24

Crooks, David, Liviu Vâlsan, Kashif Mohammad, Shawn McKee, Paul Clark, Adam Boutcher, Adam Padée, Michał Wójcik, Henryk Giemza, and Bas Kreukniet. "Operational security, threat intelligence & distributed computing: the WLCG Security Operations Center Working Group." EPJ Web of Conferences 214 (2019): 03029. http://dx.doi.org/10.1051/epjconf/201921403029.

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The modern security landscape for distributed computing in High Energy Physics (HEP) includes a wide range of threats employing different attack vectors. The nature of these threats is such that the most effective method for dealing with them is to work collaboratively, both within the HEP community and with partners further afield - these can, and should, include institutional and campus security teams. In parallel with this work, an appropriate technology stack is essential, incorporating current work on Big Data analytics. The work of the Worldwide LHC Computing Grid (WLCG) Security Operations Center (SOC) Working Group (WG) [1] is to pursue these goals to form a reference design (or guidelines) for WLCG sites of different types. The strategy of the group is to identify necessary components - starting with threat intelligence (MISP [2]) and network intrusion detection (Bro [3]), building a working model over time. We present on the progress of the working group thus far, in particular on the programme of workshops now underway. These workshops give an opportunity to engage with sites to allow the development of advice and procedures for deployment, as well as facilitating wider discussions on how to best work with trust groups at different levels. These trust groups vary in scope but can include institutes, National Grid Infrastructures and the WLCG as a whole.

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25

Belyh, A. A., and V. V. Shirokova. "ASSESSMENT OF THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE OPERATIONAL MANAGEMENT OF THE PRECINCT RAILWAY STATION." National Association of Scientists 1, no. 29(56) (July 14, 2020): 36–41. http://dx.doi.org/10.31618/nas.2413-5291.2020.1.56.229.

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This article dedicated to solving the problem in the field of operational management of the railway station using neural networks. The article outlines the basic principles of the software and examines its architecture. The technological schedule of the station attendant with the use of artificial intelligence proposed. An assessment of the impact of artificial intelligence on operational control in the operation of the control room carried out.

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26

Ilardi, Gaetano Joe. "Al Qaeda's Operational Intelligence—A Key Prerequisite to Action." Studies in Conflict & Terrorism 31, no. 12 (December 9, 2008): 1072–102. http://dx.doi.org/10.1080/10576100802508086.

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27

Farooq, Farrah. "The data warehouse virtualization framework for operational business intelligence." Expert Systems 30, no. 5 (September 7, 2012): 451–72. http://dx.doi.org/10.1111/j.1468-0394.2012.00651.x.

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28

Carter, David L., and Jeremy G. Carter. "Intelligence-Led Policing." Criminal Justice Policy Review 20, no. 3 (December 5, 2008): 310–25. http://dx.doi.org/10.1177/0887403408327381.

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Policing in the post-9/11 era is experiencing a philosophical change that is expanding community- and problem-oriented policing to include the broader philosophy of intelligence-led policing (ILP). Building on the British experience, the application of ILP to American policing has been complicated by a number of challenges. Although stimulated by 9/11, the movement toward ILP is being furthered by a number of federal public policy initiatives. As a result of these diverse demands, law enforcement must revisit operational policies and creatively adjust their organizations to reflect this new paradigm. This article provides insight on the conceptual background of ILP, public policy standards, and the integration of ILP with community policing.

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29

Kuznetsov, S. Y., and I. V. Rudenko. "BUSINESS PROCESS MANAGEMENT (BPM) IN STRATEGIC MANAGEMENT." Strategic decisions and risk management, no. 2 (June 25, 2015): 92–98. http://dx.doi.org/10.17747/2078-8886-2015-2-92-98.

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Operational management of business processes is a key success factor. We reduce tactical level to the two generic concepts: current measures (higher layers) and business processes (the lower layers). For successful execution of business strategy leading companies transform to process-oriented (tactical level) and customer-oriented organizations (strategic level). We propose classification of business processes on criteria. Widening of automated business processes enables companies to become process leaders in the competitive proposition of value to consumers. The BPM concept turns into a long-term business-culture from the tactical level to the level of strategic management. We show the stages of the development of BPM system in the enterprise. In order to achieve operational efficiency benchmarking and operational intelligence are implemented. Within operational intelligence we offer metrics of the four main functional goals.

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30

Fisher, Robert C. "Book Review: Very Special Intelligence: The Story of the Admiralty's Operational Intelligence Centre 1939–1945." International Journal of Maritime History 12, no. 2 (December 2000): 342–43. http://dx.doi.org/10.1177/084387140001200292.

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31

Stipanović, Christian. "BUSINESS INTELLIGENCE IN MAKING TOURISM ENTERPRISES COMPETITIVE." Tourism and hospitality management 11, no. 2 (December 2005): 111–19. http://dx.doi.org/10.20867/thm.11.2.10.

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In the dynamic process of creating competitive ability, tourism enterprises need to valorise new inputs (knowledge, intellectual capital) in generating advantages (quick response, quality, price). It is crucial to implement a new business philosophy based on re-engineering and earned out by encouraging innovations, asserting knowledge as a key resource, and introducing benchmarking and business intelligence. Business Intelligence represents the totality of an enterprise’s information, perceptual and operational competencies as a basis for managing change and building competitiveness. It centres on protecting the data of one enterprise, while gathering the data of others in creating the information needed for a development strategy. Tourist enterprises in Croatia have not yet realised the importance of business intelligence.

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32

Sapateiro, Cláudio Miguel, and Rui Miguel Bernardo. "Crossing Human Factors Research and Business Intelligence." International Journal of Enterprise Information Systems 16, no. 3 (July 2020): 78–92. http://dx.doi.org/10.4018/ijeis.2020070106.

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Starting from business intelligence (BI) reference models, this work proposes to extend the multi-dimensional data modelling approach to integrate human factors (HF)-related dimensions. The overall goal is to promote a fine grain understanding of the derived key performance indicators (KPIs) through an enhanced characterization of the operational level of work context. HF research has traditionally approached critical domains and complex socio-technical systems with a chief consideration of human situated action. Grounded on a review of the body of knowledge of the HF field, this work proposes the business intelligence for human factors (BI4HF) framework. It intends to provide guidance on pertinent data identification, collection methods, modelling, and integration within a BI project endeavour. BI4HF foundations are introduced, and a use case on a manufacturing industry organization is presented. The outcome of the enacted BI project referred in the use case allowed new analytical capabilities regarding newly derived and existing KPIs related to operational performance, providing insight into the value of the BI4HF framework.

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33

He, Xin Hua, Yi Nan Ma, and Qiong Wang. "Aggregation of Systematic Operational Capability of Armaments." Advanced Materials Research 756-759 (September 2013): 4439–43. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4439.

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Aggregation of systematic operational capability of armaments is significant for armaments systems' developing, constructing and using. As holistic avalanche and operational capability's nonlinear character of armaments systems, the feedback mechanism among capability indexes is researched and an aggregate method of systematic operational capability based on feedback mechanism is proposed on this paper, what's more, the method is proved effective by certain example of reconnaissance intelligence armaments systems' operational capability aggregation.

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34

Sarma, A. D. N. "A Generic Functional Architecture for Operational BI System." International Journal of Business Intelligence Research 9, no. 1 (January 2018): 64–77. http://dx.doi.org/10.4018/ijbir.2018010105.

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In recent years, Operational Business Intelligence has emerged as an important trend in the Business Intelligence (BI) market. Majority of BI application architectures are bespoke in nature which have several architectural limitations like tightly coupled, static, historic, subjective, no performance measurement of business processes, limited user access, limited analytical processing, querying and reporting features. In this article, a generic functional architecture for Operational BI systems based on software architecture principles is presented. All functional modules of the system are derived from the key features of the system and by using top down approach of software design principles. The similar functional modules are grouped into sub-systems and a set of these sub-systems constitutes overall functional architecture. The proposed architecture overcomes the limitations of traditional BI architectures.

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35

Al-Fawaeer, Moayyad, and Ayman Wael Alkhatib. "The Effect of Emotional Intelligence of Operational Team Leaders on the Performance of Team Members." Research in World Economy 11, no. 5 (September 5, 2020): 266. http://dx.doi.org/10.5430/rwe.v11n5p266.

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This study is aimed at identifying the effect of emotional intelligence with its dimensions (self-awareness, self-regulation, empathy, motivation, and social skills) on the performance of working teams with its dimensions (task performance, contextual performance, and counterproductive performance) among employees on the operational lines of industrial companies operating in the Jordanian city of Sahab. The analysis is limited to employees in those companies, and the questionnaire is used as a data collection tool, taking a simple random sample to represent the study population. In addition to the analysis of 216 questionnaires, the SPSS program is used as a data analysis tool in the study. The study emphasizes the importance of emotional intelligence dimensions for operational team leaders, especially motivation and social skills dimensions because they have a higher effect on the task performance and contextual performance levels, while all dimensions of emotional intelligence have a negative effect on counterproductive performance for operational team members.

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36

Kyper, Eric, Michael Douglas, and Roger Blake. "Using Business Intelligence for Operational Decision-Making in Call Centers." International Journal of Decision Support System Technology 4, no. 1 (January 2012): 43–54. http://dx.doi.org/10.4018/jdsst.2012010104.

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This paper proposes an operational business intelligence system for call centers. Using data collected from a large U.S. insurance company, the authors demonstrate a decision tree based solution to help the company achieve excellence through improved service levels. The initial results from this study provide insight into the factors affecting this firm’s call center service levels, and the solution developed in this paper provides two distinct advantages to managers. First, it enables them to identify key factors and the role they play in determining service levels. Second, a sliding window approach is proposed which allows managers to see the effects of resource reallocation on service levels on an on-going basis.

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37

HOGGE, SHARON M. "ROBOTIC AND ARTIFICIAL INTELLIGENCE SYSTEMS FOR THE NAVAL OPERATIONAL ENVIRONMENT." Naval Engineers Journal 99, no. 4 (July 1987): 74–86. http://dx.doi.org/10.1111/j.1559-3584.1987.tb02159.x.

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38

Davies, Huw. "Integration of strategic and operational intelligence during the Peninsular war." Intelligence and National Security 21, no. 2 (April 2006): 202–23. http://dx.doi.org/10.1080/02684520600619874.

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39

Regens, James L. "Augmenting human cognition to enhance strategic, operational, and tactical intelligence." Intelligence and National Security 34, no. 5 (February 18, 2019): 673–87. http://dx.doi.org/10.1080/02684527.2019.1579410.

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40

Panayiotopoulos, J.-C. "White, Gray and Black Operational Holes : An Artificial Intelligence Approach." Journal of Information and Optimization Sciences 13, no. 3 (September 1992): 407–25. http://dx.doi.org/10.1080/02522667.1992.10699126.

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41

Claasen, Adam. "The German Invasion of Norway, 1940: The Operational Intelligence Dimension." Journal of Strategic Studies 27, no. 1 (March 2004): 114–35. http://dx.doi.org/10.1080/0140239042000232792.

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42

Chien, Chen-Fu, Mitsuo Gen, Yongjiang Shi, and Chia-Yu Hsu. "Manufacturing intelligence and innovation for digital manufacturing and operational excellence." Journal of Intelligent Manufacturing 25, no. 5 (March 21, 2014): 845–47. http://dx.doi.org/10.1007/s10845-014-0896-5.

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43

Alexiou, Kiriakos, Efthimios G. Pariotis, Theodoros C. Zannis, and Helen C. Leligou. "Prediction of a Ship’s Operational Parameters Using Artificial Intelligence Techniques." Journal of Marine Science and Engineering 9, no. 6 (June 21, 2021): 681. http://dx.doi.org/10.3390/jmse9060681.

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The maritime industry is one of the most competitive industries today. However, there is a tendency for the profit margins of shipping companies to reduce due to an increase in operational costs, and it does not seem that this trend will change in the near future. The most important reason for the increase in operating costs relates to the increase in fuel prices. To compensate for the increase in operating costs, shipping companies can either renew their fleet or try to make use of new technologies to optimize the performance of their existing one. The software structure in the maritime industry has changed and is now leaning towards the use of Artificial Intelligence (AI) and, more specifically, Machine Learning (ML) for calculating its operational scenarios as a way to compensate the reduction of profit. While AI is a technology for creating intelligent systems that can simulate human intelligence, ML is a subfield of AI, which enables machines to learn from past data without being explicitly programmed. ML has been used in other industries for increasing both availability and profitability, and it seems that there is also great potential for the maritime industry. In this paper the authors compares the performance of multiple regression algorithms like Artificial Neural Network (ANN), Tree Regressor (TRs), Random Forest Regressor (RFR), K-Nearest Neighbor (kNN), Linear Regression, and AdaBoost, in predicting the output power of the Main Engines (M/E) of an ocean going vessel. These regression algorithms are selected because they are commonly used and are well supported by the main software developers in the area of ML. For this scope, measured values that are collected from the onboard Automated Data Logging & Monitoring (ADLM) system of the vessel for a period of six months have been used. The study shows that ML, with the proper processing of the measured parameters based on fundamental knowledge of naval architecture, can achieve remarkable prediction results. With the use of the proposed method there was a vast reduction in both the computational power needed for calculations, and the maximum absolute error value of prediction.

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44

Cardaci, Maurizio, Amelia Gangemi, Antonino Miragliotta, and Giovanni Sprini. "Operational Intelligence and School Problems Measured by Piagetian Tasks and Rhode Island Pupil Identification Scale." Psychological Reports 82, no. 3_suppl (June 1998): 1307–11. http://dx.doi.org/10.2466/pr0.1998.82.3c.1307.

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To explore the relations between operational intelligence rated on several Piagetian tasks and school learning problems identified by the Rhode Island Pupil Identification Scale by Novack, Bonaventura, and Merenda we assumed that Piagetian performances would allow predicting learning problems of first graders. 38 pupils were evaluated by three teachers, who were asked to complete the Rhode Island scale. Then the same children were individually given 8 Piagetian tests (including conservation and logical tasks) by an examiner who had no information about the pupils' other scores. Analysis showed that successful Piagetian performances were associated with ratings of successful schoolwork Analysis of variance confirmed that difficulties in operational reasoning predict occurrence of learning problems. We concluded that operational intelligence and school learning in children are closely related.

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45

Gratton, Phil. "Intelligence Challenges of the Data Rich World." Journal of Intelligence, Conflict, and Warfare 3, no. 3 (March 1, 2021): 76–79. http://dx.doi.org/10.21810/jicw.v3i3.2580.

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On November 24, 2020, Phil Gratton presented Intelligence Challenges of the Data Rich World at the 2020 CASIS West Coast Security Conference. The presentation was followed by a group panel for questions and answers. Main discussion topics included the abundance of data; non-threat related data-sets; and legislative and operational advancements towards contributing to cutting-edge intelligence processes.

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46

Ding, Jian-lan, and Bing Shi. "Analysis and Modeling of Enterprise Competitive Intelligence Based on Social Media User Comments." Entrepreneurship Research Journal 11, no. 2 (January 11, 2021): 47–69. http://dx.doi.org/10.1515/erj-2020-0206.

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Abstract Under modern information conditions, competitive intelligence has an important strategic role for an enterprise to reduce market, financial and other risks. How to correctly evaluate the value of competitive intelligence and make the right decision for an enterprise directly determines the survival and development of an enterprise. By establishing a co-occurrence relationship between the two, the core themes of corporate microblog communication are discovered. Proceeding from the basic theory of the value of competitive intelligence, it is proposed that due to the connection of business departments within the enterprise system, the transfer value of competitive intelligence within the enterprise system is changed, and a mathematical analysis method is used to establish a value transfer analysis model. The value-added process of enterprise competitive intelligence and the internal and external operational processes of the enterprise are unified, and the internal and external competitive intelligence value-added operation modes of enterprises led by different strategies are studied. This provides a new and widely-referenced operational reference for different companies to effectively formulate and implement their competition and cooperation strategies through internal and external competitive intelligence operations.

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47

Darudiato, Suparto, Sigit Wisnu Santoso, and Setiady Wiguna. "BUSINESS INTELLIGENCE: KONSEP DAN METODE." CommIT (Communication and Information Technology) Journal 4, no. 1 (May 31, 2010): 63. http://dx.doi.org/10.21512/commit.v4i1.537.

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Recently, usage of Information Technology (IT) as assisting tools in business activities is becoming more common. Business transactions are conducted with support of IT tools so that they can be well processed by system. This is only a small part of IT usage in supporting business activities. IT role can be increased by designing and implementing a system which can extract and change business information from existing operational data so that in the end it can give support in formulating business decisions. This research will discuss the concept, benefits, categories, and method of Business Intelligence implementation.Keywords: business intelligence, method, balanced scorecard

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Babushkin, Vladimir A. "LEGAL BASES OF CARRYING OUT CRIMINAL INTELLIGENCE AND SURVEILLANCE ANALYTICS ACTIVITIES BY INTERNAL AFFAIRS AGENCIES." Russian investigator 12 (December 17, 2020): 60–64. http://dx.doi.org/10.18572/1812-3783-2020-12-60-64.

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The article is devoted to topical issues of legislative regulation of the activities of internal affairs bodies in the field of operational-search analytics. The author examines the content and specifics of the activities of operational police units in this direction in the context of the development of operational-search legislation of the Russian Federation, taking into account the formation of an information society and a digital economy in the country.

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Marjanovic, Olivera. "The Importance of Process Thinking in Business Intelligence." International Journal of Business Intelligence Research 1, no. 4 (October 2010): 29–46. http://dx.doi.org/10.4018/jbir.2010100102.

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The growing field of Operational Business Intelligence (BI) has resulted in increasing interest in BI-supported Business Processes (BPs), including their management and ongoing improvement. This has led BI practitioners to consider another field–Business Process Management (BPM)–that is closely related to business performance management. However, current approaches to the BPM and operational BI integration have been limited and reduced to the problem of technical integration of BPM and BI systems. This paper argues that by adopting process- thinking in BI, further opportunities for business value creation could be discovered through systematic analysis of the non-technical aspects of BI and BPM integration, including strategy alignment, human-centered knowledge management, and ongoing improvement of BI supported processes. The authors propose a theoretical framework founded in the related research in BPM, BI, and Knowledge Management (KM) fields, describing the ways it has been used to guide ongoing empirical research in diverse case organizations across different industry sectors.

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Berner, Leonid, Vladislav Nikanorov, Sergey Marchenko, and Yury Zeldin. "ARTIFICIAL INTELLIGENCE METHODS IN GAS TRANSMISSION SYSTEM MANAGEMENT." ITNOU: Information technologies in education, science and management, no. 2 (December 2, 2020): 18–22. http://dx.doi.org/10.47501/itnou.2020.2.18-22.

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This article discusses methods of artificial intelligence used in the operational dispatch management of gas transportation systems (GTS) in regular and emergency situations: expert systems and neural networks, as well as tools that make it possible to effectively implement these methods: GTS modeling, gas supply forecasting.

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