As 5G wireless communication technology is currently deployed, an increasing amount of data is available from mobile devices out in the field. Exploiting this data, also called system traces, recent investigations sho...
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ISBN:
(纸本)9783030300333;9783030300326
As 5G wireless communication technology is currently deployed, an increasing amount of data is available from mobile devices out in the field. Exploiting this data, also called system traces, recent investigations show the potential to improve the wireless modem design and performance using datacentric approaches. Such data-centric workflows are exploratory and iterative by nature. For instance, time pattern identification is performed by domain experts to derive assumptions on potential optimizations and these assumptions are continuously refined during multiple iterations of data collection, visualization and exploration. In this context, we propose three optimizations to increase the exploration speed in iterative data-centric workflows. First, we present a methodology based on persistent memoization in order to minimize the data processing duration when additional event sequences need to be extracted from a trace. We show that up to 84.5% of the event extraction time can be spared for a typical modem trace data set. Secondly, we present a novel entropy-based data interaction technique for visual exploration of event sequences and finally, a similarity measure to perform subsequence matching in order to assist the user when identifying frequent time patterns in a trace.
Analyzing the user behaviors of multiplayer online games can help understand the sociality and characteristics of players in the virtual world. The primary task is to characterize the game life and its evolution withi...
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Analyzing the user behaviors of multiplayer online games can help understand the sociality and characteristics of players in the virtual world. The primary task is to characterize the game life and its evolution within the game. We propose a novel network-based representation, EvolutionLine Graph, which illustrates the evolving behavior of massive game players as a sequence of time-oriented transitions among various status. We design and implement a novel visual analytics system, GameLifeVis, that supports the visualization, exploration, and analysis of multi-level user behaviors in an integrated visual interface. We exemplify the efficiency of our approach with case studies on a multi-faceted dataset collected within a popular online game (15 million players) in 18 months.
Analyzing the user behavior of multiplayer online games can help understand the sociality and characteristics of players in the virtual world. The primary task is to characterize the game life and its evolution within...
详细信息
ISBN:
(纸本)9783319402598;9783319402581
Analyzing the user behavior of multiplayer online games can help understand the sociality and characteristics of players in the virtual world. The primary task is to characterize the game life and its evolution within the game. We propose a novel network-based representation, EvolutionLine Graph, that illustrates the evolving behavior of massive game players as a sequence of time-oriented transitions among various status. We design and implement a novel visual analytics system, GameLifeVis, that supports the visualization, exploration, and analysis of multi-level user behaviors in an integrated visual interface. We exemplify the efficiency of our approach with case studies on a multi-faceted dataset collected within a popular online game (15 million players) in 18 months.
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