User Behavior Pattern Analysis and Prediction Based on Mobile Phone Sensors

Song, Jiqiang; Tang, Eugene Y.; Liu, Leibo

doi:10.1007/978-3-642-15672-4_16

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…Wireless sensors can also be integrated into mobile phones (Song et al, 2010) or be wearable (Antolín et al, 2017). One variation of wearable technology is radio-frequency identification (RFID) tags.…”

Section: Wireless Sensorsmentioning

confidence: 99%

The conditions data model supporting building information models in facility management

Halmetoja¹

2019

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Purpose This paper aims to describe how building information model (BIM) and big data can be combined in the same interface for providing new value to stakeholders, such as the property owner and user, as well as property service and workplace service companies. The research presents a new concept, which shows how the BIM can be exploited efficiently during maintenance. Design/methodology/approach Initially, existing facility management (FM) processes were investigated to find out how to digitize them and identify bottlenecks. Second, BIM’s data content was explored to identify the information that could be used to streamline FM processes. Third, the potential of the active data measured in the building was evaluated. Finally, research was undertaken to find out how constantly fluctuating information can be combined with BIM objects and what kind of added value that combination could offer. The literature review was used to support the primary contribution. In addition, the research problems were described and the basics of the research were obtained by interviews. The author has interviewed 27 professionals from several stakeholders. Findings The first finding is that the BIM can serve as a platform for building use, various services and management when it has been adequately generated during the planning and construction phases and enriched before being commissioned. The other essential finding is the theory of conditions data model (CDM), which is a technical environment that combines active data with BIM. The most important advantages of BIM in FM are as follows: • Building owner attains better user satisfaction, acquires better quality and smarter services, saves energy, ensures better indoor conditions and improves building profitability. • Service providers can develop and offer new services, speed up operations, save resources and generate more profits. • The occupant gets a better user experience, faster and higher quality services and better indoor conditions. Research limitations/implications The CDM enables to generate for the real estate and construction (RE&C) sector a novel BIM-based ecosystem with standard rules, instead of every individual operator developing his/her own unique solution for BIM use in FM. This will have an impact on the entire RE&C sector’s operating methods and will have significant financial implications in the near future. Application of this research is limited to office buildings where indoor condition measuring is undertaken continuously and where the knowledge of the use cases of spaces is available. In addition, the proper BIM in the Industry Foundation Classes format must exist. The evaluation of the validity of big data is not discussed in this article. Visualization of data and content of user interfaces will be the topic of another article by the author. This article does not deal with intricate technical details, but crucial issues are defined. Originality/value The article presents a unique method for BIM use in FM. The theory of CDM (how to combine active data with BIM) is completely new and a similar solution has not been presented earlier. The theory of the presented method will be the crucial key for BIM use and will lead worldwide commissioning. Currently, the theory is under test in the practical pilot project. The results of the project will be published in the next article.

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Section: Wireless Sensorsmentioning

confidence: 99%

The conditions data model supporting building information models in facility management

Halmetoja¹

2019

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“…For example, these values can be a movement, an action, a current situation (location) and time. By supplying or classifying these four properties, the user's behavior can be analyzed and patterns can be extracted, as this model accounts for transitions between single states [12]. Parts of the models can be omitted, or added, depending on the available data and requirements of the information extraction.…”

Section: Analysing User Behavior Patternsmentioning

confidence: 99%

Providing Customised Information Panel Content Based on User Behavioral Patterns

Pisařovic¹,

Procházka²,

Vybiral³

et al. 2018

mendel

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Although mobile applications are commonly using user location and behavior to provide relevant content, public information panels usually lack the ability to adjust the content for a particular user or a group of users. Therefore, we focused on the development of information panels that are able, in combination with a mobile application, to collect anonymous location data about the users, identify key behavioral patterns and provide content that is relevant for the users in the panel vicinity. The key property of our solution is the anonymity of the collected information and privacy in general. The proposed algorithm consists of the data clustering and subsequent analysis. Described solution can be used in any public building or campus that the users visit regularly.

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“…Researchers use the mobile phone's footprint to predict the user's behavior [8], Bluetooth traces [9], Global Position System (GPS) hint [10], and smart card data [11]. In the literature, we find that some researchers use these devices for land-use identification-for instance, the demonstration of GPS data for discovering a region and sensing human activity [12], urban Wi-Fi characterization [13], land-use and landscape identification using cell-phone data [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Land-Use Map Based on Twitter Data

2017

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Location-based social media allows people to communicate and share information on a popular landmark. With millions of data records generated, it provides new knowledge about a city. The identification of land use intends to uncover accurate positions for future urban development planning. The purpose of this research is to investigate the use of social networking check-in data as a source of information to characterize dynamic urban land use. The data from this study were obtained from the social media application i.e., Twitter. Three kinds of data that are prioritized in this research are check-ins (specific location), timestamps, and a user's status text or post activities. In this study, we propose a grid-based aggregation method to divide the urban area. Two different approaches are compared-rank and clustering methods to group the place's activities. Then we utilize time distribution frequency to attain the land-use function. In this case, Makassar City, Indonesia, has been selected as the case study. An analysis shows that the check-in activity and the method we proposed can be used to group the actual land-use types.

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User Behavior Pattern Analysis and Prediction Based on Mobile Phone Sensors

Cited by 8 publications

References 10 publications

The conditions data model supporting building information models in facility management

The conditions data model supporting building information models in facility management

Providing Customised Information Panel Content Based on User Behavioral Patterns

Dynamic Land-Use Map Based on Twitter Data

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