This paper uses the grounded theory building method to investigate the differences and similarities in the evolutionary paths of three entrepreneurial ventures. Theory-based reasoning was used to select cases represen...
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ISBN:
(纸本)9781890843373
This paper uses the grounded theory building method to investigate the differences and similarities in the evolutionary paths of three entrepreneurial ventures. Theory-based reasoning was used to select cases representing different technopreneurial business models, namely product, expert service, and integrated product-service business models. Our cases emerged from a disrupted global technology corporation and they represent the economic activity emerging from classical creative destruction. We investigate the evolutionary paths of these ventures from their start-up by employing the resource-based view (RBV) approach to determine the critical change points and watershed events that guide the path of the cases. With these analyses, we are able to differentiate the performance differentials and evolutionary trajectories of the cases. The cases suggest that while the strength of the original path is strong it is possible to change the course despite path dependencies. We outline the mechanisms that facilitated these transitions (e.g., finding a suitable equity fund as a catalyst to make the change). Hence, we find that entrepreneurial ventures are not as path dependent as previous studies suggest;rather, there is managerial discretion to change the evolutionary trajectories. Our study suggests that the capabilities acquired over a long period of time in a global technology corporation contribute to the ability to change the path. We discuss the theoretical and practical implications of the critical decision points during the evolutionary trajectories of ventures.
For robotic tasks in unknown and large environments, it is important to locate targets to be manipulated by the robot in the environment. If the workspace is too large to capture at a time within the limited measureme...
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ISBN:
(数字)9781665490429
ISBN:
(纸本)9781665490429
For robotic tasks in unknown and large environments, it is important to locate targets to be manipulated by the robot in the environment. If the workspace is too large to capture at a time within the limited measurement field of visual sensors, it is necessary to explore the workspace by moving sensors. However, previous research which models almost the whole environment conduct unnecessarily exhaustive exploration for task execution. The purpose of this research is to enable robots to locate targets in a large workspace in a short time for task execution. Target exploration is formulated as a variation of a 2D black-box optimization problem. A sample-efficient exploration algorithm is proposed based on Bayesian optimization(BO) with Gaussian Process(GP). To utilize the continuity of physical motion of the camera, the algorithm for continuous search is proposed. Furthermore, to utilize prior knowledge about target positions, if available, a method of incorporating prior distribution in GP is proposed, which enables even faster exploration. Experimental results in both simulation and real environments demonstrated the efficiency of the proposed method compared to hand-crafted or naive BO-based exploration algorithms.
Acute chest pain is a common symptom of cardiovascular disease, and its data have important research value. However, the presence of missing value in medical datasets is almost inevitable, which may adversely affect t...
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Community detection partitions users in social networks into sub-groups according to structural or behavioral similarities, which had been widely adopted by a lot of applications such as friend recommendation, precisi...
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ISBN:
(纸本)9783030295516;9783030295509
Community detection partitions users in social networks into sub-groups according to structural or behavioral similarities, which had been widely adopted by a lot of applications such as friend recommendation, precision marketing, etc. In this paper, we propose a location-interest-aware community detection approach for mobile social networks. Specifically, we develop a spatial-temporal topic model to describe users' location interest, and introduce an auto encoder mechanism to represent users' location features and social network features as low-dimensional vectors, based on which a community detection algorithm is applied to divide users into sub-graphs. We conduct extensive experiments based on a real-world mobile social network dataset, which demonstrate that the proposed community detection approach outperforms the baseline algorithms in a variety of performance metrics.
The proceedings contain 71 papers. The special focus in this conference is on PLM for sustainability, Traceability and performance, PLM infrastructure and implementation processes, Capture and reuse of product and pro...
ISBN:
(纸本)9783642415005
The proceedings contain 71 papers. The special focus in this conference is on PLM for sustainability, Traceability and performance, PLM infrastructure and implementation processes, Capture and reuse of product and process information, PLM and knowledge management and Enterprise systems integration. The topics include: An analysis of a large medical device company;a few guidelines for a good usage of PLM software;components margins through the product lifecycle;a foundation for costing through-life availability provision;knowledge management for complex product development;uniting lifecycle information - from items to assets, from concepts to practice;integrated platform from CAD to CNC;improving digital engineering tools in complex product development by means of an adequate monitoring of research projects;a product model to capture and reuse ecodesign knowledge;capitalizing data, information and knowledge on mechanical experiments through ontologies;knowledge-based lifecycle management approach for product service systems (PSS);challenges in knowledge management for structuring systems;knowledge management in E-commerce mass customization;a knowledgebased collaborative platform for the design and deployment of manufacturing systems;a knowledge management approach through product lifecycle management implementation: an industrial case study;proposal of a knowledge-basedengineering methodology for mass customization;unified taxonomy for reference ontology of shape features in product model;a reference architecture for an enterprise knowledge infrastructure and a case study on the integration of GPS concepts in a PLM based industrial context.
The proceedings contain 34 papers. The topics discussed include: is virtual reality product development different? an empirical study on VR product development practices;ThrustHetero: a framework to simplify heterogen...
ISBN:
(纸本)9781450362153
The proceedings contain 34 papers. The topics discussed include: is virtual reality product development different? an empirical study on VR product development practices;ThrustHetero: a framework to simplify heterogeneous computing platform programming using design abstraction;an approach to identify use case scenarios from textual requirements specification;change-proneness of object-oriented software using combination of feature selection techniques and ensemble learning techniques;enhancing test cases generated by concolic testing;a knowledge centric approach to conceptualizing robotic solutions;making sense of actor behavior: an algebraic filmstrip pattern and its implementation;a software framework for adaptive signal analytics based on autonomic service components;modeling and coverage analysis of programs with exception handling;the value of software architecture recovery for maintenance;evolution traceability roadmap for business processes;key factors in scaling up agile team in matrix organization;and a case study exploring supply chain systems using actor based simulation.
Artificial Intelligence (AI) is rapidly advancing with a data-centered approach suitable for various domains. Nevertheless, AI faces significant challenges, particularly in data quality. Data collection from diverse s...
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ISBN:
(纸本)9798400705915
Artificial Intelligence (AI) is rapidly advancing with a data-centered approach suitable for various domains. Nevertheless, AI faces significant challenges, particularly in data quality. Data collection from diverse sources can introduce quality issues that may threaten the development of AI-enabled systems. A growing concern in this context is the emergence of data smells - issues specific to the data used in building AI models, which can have long-term consequences. In this paper, we aim at enlarging the current body of knowledge on data smells, by proposing a two-step investigation into the matter. First, we updated an existing literature review in an effort of cataloguing the currently existing data smells and the tools to detect them. Afterward, we assess the prevalence of data smells and their correlation with data quality metrics. We identify a novel set composed of 12 data smells distributed across three additional categories. Secondly, we observe that the correlation between data smells and data quality is notably impactful, exhibiting a pronounced and substantial effect, especially in highly diffused data smell instances. This research sheds light on the complex relationship between data smells and data quality, providing valuable insights into the challenges of maintaining AI-enabled systems.
Traditional approaches for facilitating the learning process in risk analysis and management in software projects usually lack practical activities in solving real problems. This is basically due to time restrictions ...
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ISBN:
(纸本)9789078677789
Traditional approaches for facilitating the learning process in risk analysis and management in software projects usually lack practical activities in solving real problems. This is basically due to time restrictions and the difficulty of simulating real scenarios where such risks occur. In this sense, games have been used as a learning resource to simulate real scenarios and allow users to learn and practice with no real risks or losses. In this paper, we present an ongoing project to develop a multi-player, persistent, Web-based game. This game, named ERISKGAME, aims to improve the learning process in risk management in software projects based on intelligent agents and fuzzy systems. The intelligent agents control the actions of the game based on fuzzy systems that store expert knowledge in fuzzy rule bases. Users play the role of software project managers who have to compete for market resources by presenting competitive budgets and software management plans. Users also have to deal with real-world problems involving risks, such as managing employees, budgets, and changes in project plans. The game is being validated in two undergraduate softwareengineering courses. This paper introduces the techniques used in the project and details ERISKGAME.
Domain model is the first model in the scope of interests of software developers. Its creation, especially for complex domain, can be very costly and time consuming, as it extensively involves domain experts. On the o...
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ISBN:
(纸本)9783319192161;9783319192154
Domain model is the first model in the scope of interests of software developers. Its creation, especially for complex domain, can be very costly and time consuming, as it extensively involves domain experts. On the other side, domain knowledge could be included in existing ontologies, and can be extracted, with the support of domain experts, from them. That way of knowledge extraction should lead to better model quality, preventing from misunderstandings between business analysts and domain experts. The paper presents an approach to business model development on the base of SUMO ontologies. The approach is explained with the use of simple, but real example. The results are promising. Domain models, created using this approach, could be perceived as valuable input for further development.
Testers often represent systems under test in input parameter models. These contain parameters with associated values. Combinations of parameter values, with one value for each parameter are potential test cases. In m...
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ISBN:
(纸本)9780769527789
Testers often represent systems under test in input parameter models. These contain parameters with associated values. Combinations of parameter values, with one value for each parameter are potential test cases. In most models, some values of two or more parameters cannot be combined. Testers must then detect and avoid or remove these conflicts. This paper proposes two new methods for automatically handling such conflicts and compares these with two existing methods, based on the sizes of the final conflict-free test suites. A test suite reduction method, usable with three of the four investigated methods is also included in the study, resulting in seven studied conflict handling methods. In the experiment, the number and types of conflicts, as well as the size of the input parameter model and the coverage criterion used, are varied. All in all, 3854 test suites with a total of 929, 158 test cases were generated. Two methods stand out as tractable and complementary. The best method (called the avoid methods) with respect to test suite size is to avoid selection of test cases with conflicts. However, this method cannot always be used. The second best method (called the replace method), removing conflicts from the final test suite, is completely general.
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