Location data analytics for space management

David Caicedo, Ashish Pandharipande

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)

Abstract

Building space accounts for one of the largest operational costs for companies. While space management has traditionally been done based on human observations, technology solutions are increasingly been considered. In this paper, we consider advanced sensing systems that provide location data at a certain spatio-Temporal granularity. Location data analytics is then considered to realize two space management applications: (i) space utilization, and (ii) workspace recommendation. Towards this end, location data is processed to determine potential workspaces. The processed historic location data is used to compute defined metrics that characterize space utilization. Filtering of real-Time location data is presented to recommend workspaces that may be potentially unoccupied and present to a user. Using datasets from an experimental office testbed, we evaluate the proposed sensing and analytics solution.

Original languageEnglish
Title of host publication2017 IEEE 13th World Congress on Services : SERVICES 2017 : 25-30 June 2017, Honolulu, Hawaii : Proceedings
EditorsRami Bahsoon, Zhixiong Chen
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages1-8
Number of pages8
ISBN (Electronic)9781538620021
DOIs
Publication statusPublished - 13 Sep 2017
Externally publishedYes
Event13th IEEE World Congress on Services (SERVICES 2017) - Honolulu, United States
Duration: 25 Jun 201730 Jun 2017
Conference number: 13
http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=62419&copyownerid=97262

Conference

Conference13th IEEE World Congress on Services (SERVICES 2017)
Abbreviated titleSERVICES 2017
CountryUnited States
CityHonolulu
Period25/06/1730/06/17
Internet address

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