Cloud resource scheduling is one of the most significant tasks in the field of big data, which is a combinatorial optimization problem in essence. Scheduling strategies based on meta-heuristic algorithms (MAs) are oft...
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Cloud resource scheduling is one of the most significant tasks in the field of big data, which is a combinatorial optimization problem in essence. Scheduling strategies based on meta-heuristic algorithms (MAs) are often chosen to deal with this topic. However, MAs are prone to falling into local optima leading to decreasing quality of the allocation scheme. Algorithms with good global search ability are needed to map available cloud resources to the requirements of the task. Honey Badger Algorithm (HBA) is a newly proposed algorithm with strong search ability. In order to further improve scheduling performance, an Improved Honey Badger Algorithm (IHBA), which combines two local search strategies and a new fitness function, is proposed in this article. IHBA is compared with 6 MAs in four scale load tasks. The comparative simulation results obtained reveal that the proposed algorithm performs better than other algorithms involved in the article. IHBA enhances the diversity of algorithm populations, expands the individual's random search range, and prevents the algorithm from falling into local optima while effectively achieving resource load balancing.
Advancements in cultural informatics have significantly influenced the way we perceive, analyze, communicate and understand culture. New data sources, such as social media, digitized cultural content, and Internet of ...
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Advancements in cultural informatics have significantly influenced the way we perceive, analyze, communicate and understand culture. New data sources, such as social media, digitized cultural content, and Internet of Things (IoT) devices, have allowed us to enrich and customize the cultural experience, but at the same time have created an avalanche of new data that needs to be stored and appropriately managed in order to be of value. Although data management plays a central role in driving forward the cultural heritage domain, the solutions applied so far are fragmented, physically distributed, require specialized IT knowledge to deploy, and entail significant IT experience to operate even for trivial tasks. In this work, we present Hydria, an online data lake that allows users without any IT background to harvest, store, organize, analyze and share heterogeneous, multi-faceted cultural heritage data. Hydria provides a zero-administration, zero-cost, integrated framework that enables researchers, museum curators and other stakeholders within the cultural heritage domain to easily (i) deploy data acquisition services (like social media scrapers, focused web crawlers, dataset imports, questionnaire forms), (ii) design and manage versatile customizable data stores, (iii) share whole datasets or horizontal/vertical data shards with other stakeholders, (iv) search, filter and analyze data via an expressive yet simple-to-use graphical query engine and visualization tools, and (v) perform user management and access control operations on the stored data. To the best of our knowledge, this is the first solution in the literature that focuses on collecting, managing, analyzing, and sharing diverse, multi-faceted data in the cultural heritage domain and targets users without an IT background.
Due to the rapid progress of digitization and the increasing importance of modern information technologies like collaboration platforms companies need to find appropriate ways to overcome the looming qualification lag...
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ISBN:
(纸本)9783031055447;9783031055430
Due to the rapid progress of digitization and the increasing importance of modern information technologies like collaboration platforms companies need to find appropriate ways to overcome the looming qualification lag of their employees. Aggravated by the demographic change, they have to ensure that the knowledge of older, experienced employees is made available to others to prevent its loss in case of retirement. This is especially relevant for small and medium-sized companies where deep personal knowledge is crucial to maintain competitiveness [6]. In this context, it is of paramount importance to enable employees to document their knowledge without interrupting their daily work routine. Knowledge acquisition, distribution and utilization have to be seamlessly integrated into the employees' daily operations. This paper presents a graph-based approach to visualize assembly knowledge and its prototypical implementation. The resulting knowledge management system has been evaluated within one medium-sized company in the domain of mechanical engineering. These results as well as future developments are discussed at the end of this case study.
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