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ENCHANCING EMPLOYEE COMPETENCE USING BEST FITTED TALENT RECOMMMENDATION ALGORITHMPROFESSIONAL PRACTICES LAB Submitted byRAMAKRISHNAN R(2016658006)MASTER OF ENGINEERINGinCOMPUTER SCIENCE AND ENGINEERING2238375231775MADRAS INSTITUTE OF TECHNOLOGYANNA UNIVERSITY: CHENNAI 600 025TABLE OF CONTENTSCHAPTER NO.TITLEPAGE NO.1AIM32OBJECTIVE3LITERATURE SURVEY REVIEW34SYSTEM STUDY4.1 EXISTING SYSTEM 4.2 PROPOSED SYSTEM775SYSTEM ARCHITETURE86ALGORITHM USED97UML DIAGRAM7.1 Use Case Diagram 7.2 Class Diagram 7.3 Sequence Diagram 7.4 Collaboration Diagram7.5 Deployment Diagram7.6 Activity Diagram7.7 E-R Diagram 101112131314158SAMPLE OUTPUT169SYSTEM SPECIFICATION8.1 HARDWARE CONFIGURATION8.2 SOFTWARE CONFIGURATION 181810CONCLUSION 1911REFERENCES19AIM To provide and ensure the employee with high utilization of learning metrics along with their inspirational trends both in technology and management skills by providing best fitted competency using adoptive dynamic algorithm.OBJECTIVETalent allocation, as a vital part of talent strategies, plays an important role in improving the utilization of talents. In this mechanism, the concepts of talent-post match degree and talent utilization rate are introduced as evaluation criteria of talent optimal allocation. Best way to predict and ensure the best competency match with standard accuracy.LITERATURE SURVEYTITLE OF THE PAPER : Researches on the best-fitted talents recommendation algorithmSYSTEM DESIGN: Based on the talent demand and the number of talents predicted by time sequence model, a dynamic planning algorithm is adopted after formula derivation to recommend best-fitted talents list.Experimental results show that this best-fitted talent recommendation mechanism possesses higher utilization and is of use to the government department in talent management.HIGHLIGHTS: Possess highest talent utilization ratio and talent-post match degreeTalent-post match degree and talent utilization ratio are introduced as evaluation criterion of talent optimal allocationCHALLENGES:Time sequence model is applied to explore the distribution law of talents and predict number of talentsALGORITHM / PERFORMANCE: Dynamic programming algorithmTITLE OF THE PAPER : Information Retrieval, Fusion, Completion, and Clustering for Employee Expertise EstimationSYSTEM DESIGN: Using a novel big data workflow with components of information retrieval and search, data fusion, matrix completion, and ordinal regression clustering.Find evidence of expertise and determine appropriate evidence weights for different queries and data sources that we merge and present in a manner consumable by businesspeople. HIGHLIGHTS:Specialization, and collective intelligence are accelerated when organizations and even society-at-large has a proper inventory of the expertise of all individuals because information and communication technologies can then be used to allocate human capitalCHALLENGES: The current output depths of expertise (very deep, deep, moderate, some, limited) are only valid within the data set. The labels have no external calibration, which could certainly have different data sources than the internal dataALGORITHM / PERFORMANCE: Interior-point algorithms or accelerated proximal gradient Machine learning algorithms (supervised learning, support vector)TITLE OF THE PAPER : Big data analysis on the relationship between the organizational career management and knowledge workers’ work involved SYSTEM DESIGN: The big data analysis technological constructs the organizational career management of knowledge type staff involved in the theoretical model of mechanism. Fair promotion, career information, pay attention to training, vocational self-cognitive and the employee's work involved in a positive correlation relationship, puts forward the corresponding organizational career management countermeasures. HIGHLIGHTS: There has significant positive correlation between provide profession information anon knowledge staffwork involved.There has significant positive correlation on knowledge staff work involved attention to training and knowledge staff work involved.There has significant positive correlation between professional identify and knowledge staff involved.CHALLENGES:Dedicatedly drives and will focus on specific attributesALGORITHM / PERFORMANCE: OCM(Organization Career Management) methodology.TITLE OF THE PAPER : Competence Management as a Dynamic Capability: A Strategic Enterprise System for a Knowledge-Intensive Project OrganizationSYSTEM DESIGN: Competence typology and Level 5 Leadership concepts fit well in guiding competence management in dynamic service economy markets. Competence management should aim towards customer demand and employee interests rather than only focusing on current strengthsHIGHLIGHTS:Suggest that strategic Competence management should aim towards customer demand and employee interests rather than focusing on current strengthsCHALLENGES:Better integration and visualization of customer demand in competence management contextALGORITHM / PERFORMANCE: Research Approach(Case description, Data collection, Data analysis), Dynamic Competence Capability Lindgren’s competence typology and Collins’ hedgehog concept TITLE OF THE PAPER : The Influence of Knowledge Management Tools Utilization Towards Knowledge Management ReadinessSYSTEM DESIGN:Analyzes factors related to knowledge management tools utilization within the organization as antecedents towards knowledge management implementationreadiness.HIGHLIGHTS:The variable Portal for Client and Service providers as well as Internal KM Repository Tool were classified as having strong variance, while the variable KM readiness was classified as one with weak varianceCHALLENGES:The use of Internal Collaboration Tool did not carry a significant influence on the use of Internal KM Repository Tool. Additionally, the prior did not have a significant effect on the use of Portal for Client and Service Provider as well.ALGORITHM / PERFORMANCE: Partial Least Square Structural Equation Modelling (PLS-SEM)SYSTEM STUDY4.1 EXISTING SYSTEMBased on the talent demand and the number of talents predicted by time sequence model, a dynamic planning algorithm is adopted after formula derivation to recommend best-fitted talents list.Not focus on an overall mechanism of talent education, recruit, and allocation in the project4.2 PROPOSED SYSTEMEffectively adopted within the project and also for the future requirement and prediction in current market trends.Useful to adopt in Recruitment, talent education in Organization both in technical and non-technical demands.Enhance the best-fitted competency to employee to achieve their career goal along with their passion towards anization investing (return in investment index) in talent development will be optimized in cost.Satisfaction index for the employee will drastically increase.ARCHITECTURE OF THE PROPOSED SYSTEMALGORITHM USEDUML DIAGRAMUse Case DiagramClass DiagramSequence DiagramCollaboration DiagramDeployment DiagramActivity DiagramE-R DiagramSAMPLE OUTPUTSYSTEM SPECIFICATION9.1 HARDWARE REQUIREMENTDual-core 64-bit processor8 GB of memoryUp to 24 GB of internal storage (Kony Visualizer: 4GB, Android SDK: 2GB, Windows SDK: 4GB, BlackBerry NDK: 4GB, plus ample space for multiple complex projects)Network interface cardNameSoftwareOperating SystemWindows 10, Windows 8.1 Update, Windows 8, and Windows 7.Front EndASP MVC 5 .NET Framework, Microsoft Visual Studio 2016 Back EndMicrosoft SQL Server 20129.2 SOFTWARE REQUIREMENTCONCLUSIONEffectively adopted within the project and also for the future requirement and prediction in current market trends.Useful to adopt in Recruitment, talent education in Organization both in technical and non-technical demands.Enhance the best-fitted competency to employee to achieve their career goal along with their passion towards anization investing(return in investment index) in talent development will be optimized in cost.Satisfaction index for the employee will drastically increase. REFERENCESWilliam A. Schiemann, et al, From talent management to talent optimization, Journal of World Business, No.49, 281–288, 2014. Sanne Nijs, et al, A multidisciplinary review into the definition, operationalization, and measurement of talent, Journal of World Business, No.49, 180-191, 2014. Gutteridge T G. “ Organizational career development systems?The state of the practice” ,San Francisco: Jossey-Bass Publishers,1986.50-95. R. L. Martin, “The rise (and likely fall) of the talent economy,” Harvard Bus. Rev., Oct. 2014.K. R. Varshney, V. Chenthamarakshan, S. W. Fancher, J. Wang, D. Fang, and A. Mojsilovic, “Predicting employee ? expertise for talent management in the enterprise,” in KDD, 2014, pp. 1729–1738.Herriot, P, Gibbons, P, Pemberton, C, Jackson, P. R.. “An Em-pirical Model of Managerial Careers in Organizations”. BritishJournal of Management, 1994, (5): 113-121. Crabtree MJ. “Employees Perception of Career Management Practices: The Development of a New Measure”Journal of Career Assessment,1999. [4] SalehSD, Hosek J. “Job involvement: concepts and measurements”.Academy of Management Journal,1976,19:213-224. ................
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