The commoditization of sensors and communication networks is enabling vast quantities of data to be generated by and collected from cyber-physical systems. This “Internetof-Things” (IoT) makes possible new business opportunities, from usage-based insurance to proactive equipment maintenance. While many technology vendors now offer “Big Data” solutions, a challenge for potential customers is understanding quantitatively how these solutions will work for IoT use cases. This paper describes a benchmark toolkit called IoTAbench for IoT Big Data scenarios. This toolset facilitates repeatable testing that can be easily extended to multiple IoT use cases, including a user’s specific needs, interests or dataset. We demonstrate the benchmark via a smart metering use case involving an eight-node cluster running the HPVertica analytics platform. The use case involves generating, loading, repairing and analyzing synthetic meter readings. The intent of IoTAbench is to provide the means to perform “apples-to-apples” comparisons between different sensor data and analytics platforms. We illustrate the capabilities of IoTAbench via a large experimental study, where we store 22.8 trillion smart meter readings totaling 727 TB of data in our eight-node cluster.
This paper investigates different ways in which players have been categorized in game research literature in order to distinguish relevant customer segments for designing and marketing of game’s value offerings. This paper adopts segmentation and marketing theory as its bases of analysis. The goal is to synthesize the results of various studies and to find the prevailing concepts, combine them, and draw implications to further studies and segmentation of the player base.
The research process for this study proceeded from large literature search, to author- centric (Webster & Watson 2002) identification and categorization of previous works based on the established factors of segmentation (demographic, psychographic, and behavioral variables) in marketing theory. The previous works on player typologies were further analyzed using concept-centric approach and synthesized according to common and repeating factors in the previous studies.
The results indicate that player typologies in previous literature can be synthesized into seven key dimensions: Skill, Achievement, Exploration, Sociability, Killer, Immersion and In-game demographics. The paper highlights for further studies the self-fulfilling and self-validating nature of the current player typologies because their relatively high use in game design practices as well as discusses the role of game design in segmentation of players.