Proposal of object management system for applying to existing object storage furniture

Mayama, Katsuhiro; Skulkittiyut, Weerachai; Ando, Yoshinobu; Yoshimi, Takashi; Mizukawa, Makoto

doi:10.1109/sii.2011.6147460

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…External data storage can be automatically backed up to prevent users from losing data, and backups that are securely saved at the back-end server can be easily transferred and restored. In addition, information security is guaranteed because the physical location cannot be obtained from the disk drive (Mayama et al 2011).…”

Section: Cloud Storage For Big Data Storagementioning

confidence: 99%

Big Data and Cloud Computing

et al. 2019

Manual of Digital Earth

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Big data emerged as a new paradigm to provide unprecedented content and value for Digital Earth. Big Earth data are increasing tremendously with growing heterogeneity, posing grand challenges for the data management lifecycle of storage, processing, analytics, visualization, sharing, and applications. During the same time frame, cloud computing emerged to provide crucial computing support to address these challenges. This chapter introduces Digital Earth data sources, analytical methods, and architecture for data analysis and describes how cloud computing supports big data processing in the context of Digital Earth. Keywords Geoscience • Spatial data infrastructure • Digital transformation • Big data architecture 9.1 Introduction Digital Earth refers to the virtual representation of the Earth we live in. It represents the Earth in the digital world from data to model. Data are collected and models are abstracted to build the digital reality. Massive amounts of data are generated from various sensors deployed to observe our home planet while building Digital Earth. The term "big data" was first presented by NASA researchers to describe the massive amount of information that exceeds the capacities of main memory, local disk, and even remote disk (Friedman 2012). According to the National Institute of Standards and Technology (NIST), "Big Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, information-driven world" (Chang and Grady 2015). This definition refers to the bounty of digital data from various data sources in the context of Digital Earth, which focus on big data's geographical aspects of social information, Earth observation (EO), sensor observation service (SOS), cyber infrastructure (CI), social media and business information (Guo 2017; Guo et al. 2017; Yang et al. 2017a, b). Digital Earth data are collected from satellites,

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Section: Cloud Storage For Big Data Storagementioning

confidence: 99%

Big Data and Cloud Computing

et al. 2019

Manual of Digital Earth

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“…To provide an appropriate place for tidy-up service, object categories, object usage status and trajectory information is needed. In our previous research [4,5], we proposed a daily-life object information management system to collect, preprocessing and manage the necessary systematically. It has five parts: hardware, software, applications, server and database.…”

Section: Background Knowledge and Related Researchmentioning

confidence: 99%

Object Trajectory Information for Robotic Service in Intelligent Space

Skulkittiyut

Yamaguchi

Mizukawa³

2014

AMM

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Implementation of robotic service is needed extremely enormous of information including environment, place, user and object knowledge. It is extremely difficult for a robot to do tasks ordered by a human without having some basic knowledge or information. This paper proposes a method to create object knowledge focusing on an object and objects place relationship for the robotic service namely tidy-up service, in which a robot is asked to take objects such as books, cups, dishes on a table to appropriate places automatically. In details, as the first phase, we conduct a questionnaire to collect the trajectories in term of places of individual object from the participants. Based on the collected object trajectory information, we are able to build Markov chain model of the object which the states are possible places and transition probabilities are the probability that the object moved from one place to other places. In final, we are able to use the transition probability including the Markov chain model to predict and provide the next appropriate place. The result showed that the proposed approach is efficient in creating object trajectory as knowledge, hence, helping the robots to and to provide intuitive service.

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