The Experts below are selected from a list of 30984 Experts worldwide ranked by ideXlab platform
Van Der Goes Maurits - One of the best experts on this subject based on the ideXlab platform.
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From Talents to Team: Matching supply and demand with algorithms
2020Co-Authors: Van Der Goes MauritsAbstract:The globalizing economy with its new goods and services, knowledge spread, and competition for talent is an increasing complexity for organizations, which requires organizations to adapt more quickly. Organizations are essential to society, as people are more productive in groups. For their continuity, it is important that organizations continuously keep adapting to their environment. The new economy requires agile organizations that can quickly (co-)produce customized responses to the demands of the market. At the present moment, the hierarchical organizational structures face limits of their usefulness and are being replaced by a lean form of organization. In their place, these decentralized organizations are growing to overcome the limitations of these hierarchical structures. The coronavirus pandemic enforced a working from home policy that strongly decreased the access to work. This development stimulated organizations to move to decentralized organizing with a marketplace for team formation. The introduction of self-managing teams enables this desired dynamic and leads to an agile organization. Virtualization has further enriched and diversified these teams. These self-managing teams are useful for constructive conflict, diversity, innovation, performance gains, and synergy. The coordination of work changes with the introduction of self-managing teams. These teams in here in a strong individual choice of people, which is both a benefit and a risk for people and organizations. A digital marketplace is a supporting institution that can provide overview, insights, and access to several opportunities by combining supply and demand of teams and talents across organizational borders. Connecting workers to relevant teams is also the main performance criterion. Still, bounded rationality can result in non-optimal individual choices on the marketplace and a one-size-fits-all approach does not match the preferences of the heterogeneous workers. A filter within the marketplace is needed to decrease the obstacles of decentral coordination of activities, however, there is limited research available on this subject. This research follows the theory of design science research with the three cycles. The design objective is to support users in self-managing team formation with a recommender system and custom filtering algorithms. This is achieved with twelve design artifacts: personalization, criteria and objectives, recommender system, Data engineering, ETL script, Logical Data Model, custom filtering algorithms, Data science, validation queries, hybrid filtering algorithm, DevOps, and advice on UX design and usage. The designed system was built with the requirements of the program of business demands. The constraints for the recommender system with custom algorithms are personalized recommendations for each worker and the incorporation of platform rules. The objectives are regular updates, scalable, open-source, and easy to set up.Complex Systems Engineering and Managemen
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From Talents to Team: Matching supply and demand with algorithms
2020Co-Authors: Van Der Goes MauritsAbstract:The globalizing economy with its new goods and services, knowledge spread, and competition for talent is an increasing complexity for organizations, which requires organizations to adapt more quickly. Organizations are essential to society, as people are more productive in groups. For their continuity, it is important that organizations continuously keep adapting to their environment. The new economy requires agile organizations that can quickly (co-)produce customized responses to the demands of the market. At the present moment, the hierarchical organizational structures face limits of their usefulness and are being replaced by a lean form of organization. In their place, these decentralized organizations are growing to overcome the limitations of these hierarchical structures. The coronavirus pandemic enforced a working from home policy that strongly decreased the access to work. This development stimulated organizations to move to decentralized organizing with a marketplace for team formation. The introduction of self-managing teams enables this desired dynamic and leads to an agile organization. Virtualization has further enriched and diversified these teams. These self-managing teams are useful for constructive conflict, diversity, innovation, performance gains, and synergy. The coordination of work changes with the introduction of self-managing teams. These teams in here in a strong individual choice of people, which is both a benefit and a risk for people and organizations. A digital marketplace is a supporting institution that can provide overview, insights, and access to several opportunities by combining supply and demand of teams and talents across organizational borders. Connecting workers to relevant teams is also the main performance criterion. Still, bounded rationality can result in non-optimal individual choices on the marketplace and a one-size-fits-all approach does not match the preferences of the heterogeneous workers. A filter within the marketplace is needed to decrease the obstacles of decentral coordination of activities, however, there is limited research available on this subject. This research follows the theory of design science research with the three cycles. The design objective is to support users in self-managing team formation with a recommender system and custom filtering algorithms. This is achieved with twelve design artifacts: personalization, criteria and objectives, recommender system, Data engineering, ETL script, Logical Data Model, custom filtering algorithms, Data science, validation queries, hybrid filtering algorithm, DevOps, and advice on UX design and usage. The designed system was built with the requirements of the program of business demands. The constraints for the recommender system with custom algorithms are personalized recommendations for each worker and the incorporation of platform rules. The objectives are regular updates, scalable, open-source, and easy to set up.Complex Systems Engineering and Management (CoSEM
Shashi Shekhar - One of the best experts on this subject based on the ideXlab platform.
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lagrangian xgraphs a Logical Data Model for spatio temporal network Data a summary
International Conference on Conceptual Modeling, 2014Co-Authors: Venkata M V Gunturi, Shashi ShekharAbstract:Given emerging diverse spatio temporal network (STN) Datasets, e.g., GPS tracks, temporally detailed roadmaps and traffic signal Data, the aim is to develop a Logical Data-Model which achieves a seamless integration of these Datasets for diverse use-cases (queries) and supports efficient algorithms. This problem is important for travel itinerary comparison and navigation applications. However, this is challenging due to the conflicting requirements of expressive power and computational efficiency as well as the need to support ever more diverse STN Datasets, which now record non-decomposable properties of n-ary relations. Examples include travel-time and fuel-use during a journey on a route with a sequence of coordinated traffic signals and turn delays. Current Data Models for STN Datasets are limited to representing properties of only binary relations, e.g., distance on individual road segments. In contrast, the proposed Logical Data-Model, Lagrangian Xgraphs can express properties of both binary and n-ary relations. Our initial study shows that Lagrangian Xgraphs are more convenient for representing diverse STN Datasets and comparing candidate travel itineraries.
Venkata M V Gunturi - One of the best experts on this subject based on the ideXlab platform.
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lagrangian xgraphs a Logical Data Model for spatio temporal network Data a summary
International Conference on Conceptual Modeling, 2014Co-Authors: Venkata M V Gunturi, Shashi ShekharAbstract:Given emerging diverse spatio temporal network (STN) Datasets, e.g., GPS tracks, temporally detailed roadmaps and traffic signal Data, the aim is to develop a Logical Data-Model which achieves a seamless integration of these Datasets for diverse use-cases (queries) and supports efficient algorithms. This problem is important for travel itinerary comparison and navigation applications. However, this is challenging due to the conflicting requirements of expressive power and computational efficiency as well as the need to support ever more diverse STN Datasets, which now record non-decomposable properties of n-ary relations. Examples include travel-time and fuel-use during a journey on a route with a sequence of coordinated traffic signals and turn delays. Current Data Models for STN Datasets are limited to representing properties of only binary relations, e.g., distance on individual road segments. In contrast, the proposed Logical Data-Model, Lagrangian Xgraphs can express properties of both binary and n-ary relations. Our initial study shows that Lagrangian Xgraphs are more convenient for representing diverse STN Datasets and comparing candidate travel itineraries.
Manuel Hirsch - One of the best experts on this subject based on the ideXlab platform.
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an evolving fuzzy inference system for extraction of rule set for planning a product service strategy
Information Technology & Management, 2017Co-Authors: David Opresnik, Maurizio Fiasche, Marco Taisch, Manuel HirschAbstract:Manufacturing enterprises are collaborating among each other in manufacturing service ecosystems (MSE) with the objective to compose and provision numerous product---services (P---S) on the market. However, many paramount processes outset much before the actual composition, like the strategy planning of those P---S. Such decisions are usually full of ambiguities with complex sets of decisional possibilities, which are extremely hard to encompass even within a decision support system. Thus, the aim of this article is to undergird the development of an effective decision support system (DSS) for solving the challenge of planning a P---S strategy within a MSE, as well to present and apply a relative novel fuzzy inference technique, in order to build the DSS in question. This is achieved by first designing the Logical Data Model that conceptualizes the context of planning a P---S strategy within a MSE, secondly by designing the actual business intelligence (BI) sets of rules and thirdly to build a DSS and test its Data. As the input Data needed to plan a strategy are often intangible, without a clear delineation among classes (e.g. "Market_1 is more competitive than Market_2"), with more than just binary values that can also overlap among each other and can be expressed using human language, a fuzzy based inference system is used to build the BI rules set. The DSS provides answers to three central uncertainties in P---S strategy planning expressed in the article as performance questions.
Kasidit Chanchio - One of the best experts on this subject based on the ideXlab platform.
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Data collection and restoration for heterogenenous process migration
Software - Practice and Experience, 2002Co-Authors: Kasidit Chanchio, Xianhe SunAbstract:This study presents a practical solution for Data collection and restoration to migrate a process written in high-level stack-based languages such as C and Fortran over a network of heterogeneous computers. We first introduce a Logical Data Model, namely the Memory Space Representation (MSR) Model, to recognize complex Data structures in process address space. Then, novel methods are developed to incorporate the MSR Model into a process, and to collect and restore Data efficiently. We have implemented prototype software and performed experiments on different programs. Experimental and analytical results show that: (1) a user-level process can be migrated across different computing platforms; (2) semantic information of Data structures in the process's memory space can be correctly collected and restored; (3) costs of Data collection and restoration depend on the complexity of the MSR graph in the memory space and the amount of Data involved; and (4) the implantation of the MSR Model into the process is not a decisive factor of incurring execution overheads. With appropriate program analysis, we can practically achieve low overhead.
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Data collection and restoration for heterogeneous process migration
International Parallel and Distributed Processing Symposium, 2001Co-Authors: Kasidit ChanchioAbstract:This study presents a practical solution for Data collection and restoration to migrate a process written in high level stack-based languages such as C and Fortran over a network of heterogeneous computers. We study a Logical Data Model which recognizes complex Data structures in process address space. Then, novel methods are developed to incorporate the Model into a process and to collect and restore Data efficiently. We have implemented a prototype software and performed experiments on different programs. Experimental and analytical results show that (I) a user-level process can be migrated across different computing platforms, (2) semantic information of Data structures in the process's memory space can be correctly collected and restored, (3) the costs of Data collection and restoration depend on the complexity of the Logical Model representing the process's Data structures and the amount of Data involved and (4) the implantation of the Data collection and restoration mechanisms into the process is not a decisive factor of incurring execution overheads; with appropriate program analysis, we can achieve practically low overhead.
