The Experts below are selected from a list of 207 Experts worldwide ranked by ideXlab platform
Ranjeet Kumar Ranjan - One of the best experts on this subject based on the ideXlab platform.
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Integration of Goal Hierarchies Built Using Multiple OLAP Query Sessions
Soft Computing in Data Analytics, 2018Co-Authors: N Parimala, Ranjeet Kumar RanjanAbstract:A Goal Hierarchy consists of a strategic Goal, decision Goals, information Goals and tasks. The Goal Hierarchy can provide a decision maker different ways in which the data can be analysed in order to achieve a strategic Goal. In our earlier work, we built a Goal Hierarchy for a query session, using OLAP query recommendation technique. It is possible to build more than one Goal Hierarchy for a given strategic Goal if multiple query sessions are used. In this paper, we propose an integration technique. It is a mixed approach where decision Goals and information Goals are integrated top-down and tasks are integrated bottom-up. The result of integration of multiple Goal hierarchies is a new Goal Hierarchy with a unified view of analysis in order to identify the different perspectives of analysis in a single diagram.
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a bottom up approach for creating Goal Hierarchy using olap query recommendation technique
International Journal of Business Information Systems, 2018Co-Authors: Ranjeet Kumar Ranjan, N ParimalaAbstract:Traditionally, data warehouses have been designed guided by an analysis of the underlying data sources. On the other hand, Goal-oriented approaches build a Goal decomposition structure which is further mapped to a data warehouse model. Goal-oriented approaches address the requirements of the decision makers. In this paper, we propose to build a Goal decomposition structure for traditional data warehouses. As a result it can be assessed whether the existing data warehouse model satisfies the decision makers' requirements. The approach is bottom-up wherein the tasks are identified first and the path to the strategic Goal is built later. The tasks themselves are identified using OLAP query recommendation technique.
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identifying dissimilar olap query session for building Goal Hierarchy
2018Co-Authors: N Parimala, Ranjeet Kumar RanjanAbstract:Traditionally, a Goal-oriented approach follows the Goal decomposition technique to build a Goal Hierarchy in order to identify the schema for a data warehouse. In our earlier work, using reverse engineering approach, a Goal Hierarchy was built for an existing data warehouse schema using a single query session. The tasks of this Hierarchy address some part of the warehouse. In this paper, we address the issue of identifying the next session to build a Goal Hierarchy. The sessions which provide the tasks and information Goals distinct from existing Goal Hierarchy are desirable. To identify such a session, we define distance between sessions. The session whose distance from the current session is maximum is picked up.
N Parimala - One of the best experts on this subject based on the ideXlab platform.
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Integration of Goal Hierarchies Built Using Multiple OLAP Query Sessions
Soft Computing in Data Analytics, 2018Co-Authors: N Parimala, Ranjeet Kumar RanjanAbstract:A Goal Hierarchy consists of a strategic Goal, decision Goals, information Goals and tasks. The Goal Hierarchy can provide a decision maker different ways in which the data can be analysed in order to achieve a strategic Goal. In our earlier work, we built a Goal Hierarchy for a query session, using OLAP query recommendation technique. It is possible to build more than one Goal Hierarchy for a given strategic Goal if multiple query sessions are used. In this paper, we propose an integration technique. It is a mixed approach where decision Goals and information Goals are integrated top-down and tasks are integrated bottom-up. The result of integration of multiple Goal hierarchies is a new Goal Hierarchy with a unified view of analysis in order to identify the different perspectives of analysis in a single diagram.
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a bottom up approach for creating Goal Hierarchy using olap query recommendation technique
International Journal of Business Information Systems, 2018Co-Authors: Ranjeet Kumar Ranjan, N ParimalaAbstract:Traditionally, data warehouses have been designed guided by an analysis of the underlying data sources. On the other hand, Goal-oriented approaches build a Goal decomposition structure which is further mapped to a data warehouse model. Goal-oriented approaches address the requirements of the decision makers. In this paper, we propose to build a Goal decomposition structure for traditional data warehouses. As a result it can be assessed whether the existing data warehouse model satisfies the decision makers' requirements. The approach is bottom-up wherein the tasks are identified first and the path to the strategic Goal is built later. The tasks themselves are identified using OLAP query recommendation technique.
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identifying dissimilar olap query session for building Goal Hierarchy
2018Co-Authors: N Parimala, Ranjeet Kumar RanjanAbstract:Traditionally, a Goal-oriented approach follows the Goal decomposition technique to build a Goal Hierarchy in order to identify the schema for a data warehouse. In our earlier work, using reverse engineering approach, a Goal Hierarchy was built for an existing data warehouse schema using a single query session. The tasks of this Hierarchy address some part of the warehouse. In this paper, we address the issue of identifying the next session to build a Goal Hierarchy. The sessions which provide the tasks and information Goals distinct from existing Goal Hierarchy are desirable. To identify such a session, we define distance between sessions. The session whose distance from the current session is maximum is picked up.
John G Wacker - One of the best experts on this subject based on the ideXlab platform.
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a theoretical model of manufacturing lead times and their relationship to a manufacturing Goal Hierarchy
Decision Sciences, 1996Co-Authors: John G WackerAbstract:In recent years, manufacturing firms have realized that a new, higher level of global competition causes them to compete simultaneously on multiple manufacturing Goals, such as quality, delivery, cost, and flexibility. In response to this realization, considerable research now focuses on the relationship of manufacturing improvement programs to manufacturing Goals. However, to date, this research has not investigated the specific underlying statistical relationships between manufacturing Goals and the shop floor. This study investigates manufacturing lead time linkages with manufacturing programs and manufacturing Goals. The basic purpose of this study is to understand and explain how programs affect the elements of manufacturing lead time and how manufacturing lead time affects manufacturing Goal capabilities. By understanding these linkages, managers can logically trace the effects of specific programs to their eventual effects on manufacturing Goal capabilities. This study's most important finding is that statistical variations in the elements of lead time cause a tendency for certain manufacturing Goals to be more difficult to control and achieve than others because of canonical relationships of lead time variances. To control these lead time variances, successful firms concentrate their early program targets first on achieving “fitness for use” quality, followed by delivery reliability, short delivery lead time and cost, current product flexibility, and lastly, new product flexibility. This study mathematically illustrates which improvement programs most affect manufacturing Goals through their relationship to manufacturing lead time variance reduction. It suggests that firms improve Goal performance by initially targeting improvement through setup time reduction programs, defect reduction programs, and preventive maintenance programs, to facilitate quality improvements. By targeting specific programs and their related lead time variances, firms improve their manufacturing facility competitiveness with minimum obstacles.
Maged G Iskander - One of the best experts on this subject based on the ideXlab platform.
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Exponential Membership Functions in Fuzzy Goal Programming: A Computational Application to a Production Problem in the Textile Industry
American Journal of Computational and Applied Mathematics, 2020Co-Authors: Maged G IskanderAbstract:The linear membership function is considered the most common type that is used in fuzzy Goal programs. In this paper, the exponential membership function, whether with increasing or with decreasing rate of change, is used. Each of the two types is utilized within a fuzzy Goal program. Two main forms of fuzzy Goal program are implemented. The first is based on the lexicographical minimization, while the second is based on a preemptive Goal Hierarchy. A computational comparison between the two forms is carried out on a production planning problem in the textile industry. This problem was in the form of fuzzy linear programming, and it is amended to be in the form of fuzzy Goal programming.
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fuzzy Goal and possibility programming with imprecise Goal Hierarchy
International Journal of Operational Research, 2016Co-Authors: Maged G IskanderAbstract:In this paper, the fuzzy Goal programming is investigated when both the coefficients and the aspiration levels are considered fuzzy numbers with either trapezoidal or triangular membership functions. The possibility programming approach has been utilised in the case of exceedance possibility and the case of strict exceedance possibility. In many situations, the decision-maker cannot set a precise priority structure for the possibility functions of the fuzzy Goals. A membership function for the imprecise relation between different pairs of possibilities has been defined. This function reflects a scale for the degree of importance between any pair of Goals. This scale starts from 'almost not important' to 'certainly more important'. Accordingly, there are two types of the membership functions. The first represents the possibility functions of all fuzzy Goals, while the second is the membership functions of the imprecise importance relations. The weighted max-min approach is utilised for the two types. The suggested approach is illustrated by a numerical example.
Glyn W Humphreys - One of the best experts on this subject based on the ideXlab platform.
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the effect of action Goal Hierarchy on the coding of object orientation in imitation tasks evidence from patients with parietal lobe damage
Cognitive Neuropsychology, 2008Co-Authors: Claudia Chiavarino, Ian A Apperly, Glyn W HumphreysAbstract:In order to explore parietal patients’ difficulties in the processing of orientation information, we asked parietal patients (N ¼ 8) and healthy and brain-damaged controls to imitate multicomponent actions where object orientation was one component. In Experiment 1 orientation was not the most relevant aspect of the action to be imitated, and the parietal group showed significant difficulties in processing object orientation. However, in Experiment 2, where orientation was placed at the top end of the Goal Hierarchy, the parietal group were able to process stimulus orientation sufficiently to place it within the Goal Hierarchy of the action and to reproduce it accurately. We conclude that patients with parietal lesions might be able to include object orientation in a Goal Hierarchy, but if their processing of orientation information is impaired they might be disproportionately prone to errors when object orientation is lower in the Goal Hierarchy and so not prioritized for processing resources.
