SeTraStream: Semantic-Aware Trajectory Construction over Streaming Movement Data

Yan, Zhixian; Giatrakos, Nikos; Katsikaros, Vangelis; Pelekis, Nikos; Theodoridis, Yannis

doi:10.1007/978-3-642-22922-0_22

Cited by 30 publications

(24 citation statements)

References 30 publications

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“…Similar issues exist in the mobile phone data to certain extent, depending on the accuracy and resolution of the spatially triangulated locations of the anonymous phone users. Nonetheless, finding smart techniques to link the association rules of semanticenriched land use and POI information of the diverse areas that individuals visit is an open challenge for estimating the activity types that individuals engage in [45,46]. And this information is extremely valuable to put mobile phone data into the service of urban and transportation planning.…”

Section: Inferring Human Activitiesmentioning

confidence: 99%

A review of urban computing for mobile phone traces

Jiang

Fiore

Yang

et al. 2013

Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing

168

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In this work, we present three classes of methods to extract information from triangulated mobile phone signals, and describe applications with different goals in spatiotemporal analysis and urban modeling. Our first challenge is to relate extracted information from phone records (i.e., a set of time-stamped coordinates estimated from signal strengths) with destinations by each of the million anonymous users. By demonstrating a method that converts phone signals into small grid cell destinations, we present a framework that bridges triangulated mobile phone data with previously established findings obtained from data at more coarse-grained resolutions (such as at the cell tower or census tract levels). In particular, this method allows us to relate daily mobility networks, called motifs here, with trip chains extracted from travel diary surveys. Compared with existing travel demand models mainly relying on expensive and less-frequent travel survey data, this method represents an advantage for applying ubiquitous mobile phone data to urban and transportation modeling applications. Second, we present a method that takes advantage of the high spatial resolution of the triangulated phone data to infer trip purposes by examining semantic-enriched land uses surrounding destinations in individual's motifs. In the final section, we discuss a portable computational architecture that allows us to manage and analyze mobile phone data in geospatial databases, and to map mobile phone trips onto spatial networks such that further analysis about flows and network performances can be done. The combination of these three methods demonstrate the state-of-the-art algorithms that can be adapted to triangulated mobile phone data for the context of urban computing and modeling applications.

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Section: Inferring Human Activitiesmentioning

confidence: 99%

A review of urban computing for mobile phone traces

Jiang

Fiore

Yang

et al. 2013

Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing

168

View full text Add to dashboard Cite

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“…Approaches in [48,60,61] Supervised [9,47,59] of full sequences [10,27,38] Figure 1: Taxonomy of related work. For the sake of brevity, we cite in this figure at most three of the references that relate to our work.…”

Section: Related Workmentioning

confidence: 99%

“…The "unsupervised" branch can still be further divided into heuristic techniques and general purpose methods based on optimizing a suitable criterion function. The "heuristic" (or application oriented) branch encompasses techniques such as those presented in [48,60,61]. These techniques generally require an important number of input parameters to be tuned.…”

Section: Related Workmentioning

confidence: 99%

Warped K-Means: An algorithm to cluster sequentially-distributed data

Leiva

Vidal

2013

Information Sciences

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Many devices generate large amounts of data that follow some sort of sequentiality, e.g., motion sensors, e-pens, eye trackers, etc. and often these data need to be compressed for classification, storage, and/or retrieval tasks. Traditional clustering algorithms can be used for this purpose, but unfortunately they do not cope with the sequential information implicitly embedded in such data. Thus, we revisit the wellknown K-means algorithm and provide a general method to properly cluster sequentially-distributed data. We present Warped K-Means (WKM), a multi-purpose partitional clustering procedure that minimizes the Sum of Squared Error criterion, while imposing a hard sequentiality constraint in the classification step. We illustrate the properties of WKM in three applications, one being the segmentation and classification of human activity. WKM outperformed five state-of-the-art clustering techniques to simplify data trajectories, achieving a recognition accuracy of near 97%, which is an improvement of around 66% over their peers. Moreover, such an improvement came with a reduction in the computational cost of more than one order of magnitude.

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“…Each segment is corresponding to a "specific" concept or activity (e.g., one segment is at home and the next one is in outside; one segment is on "walking", while the subsequence one is on "cycling"). Because of such meaningful understanding, the mobile data segmentation has received a lot of attention recently in various mobile sensors, e.g., GPS-based trajectory segmentation [1,3,18,15], accelerometer-based motion segmentation [7,6,14].…”

Section: Introductionmentioning

confidence: 99%

An adaptive approach for online segmentation of multi-dimensional mobile data

Guo

Yan

Aberer

2012

Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access

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With increasing availability of mobile sensing devices including smartphones, online mobile data segmentation becomes an important topic in reconstructing and understanding mobile data. Traditional approaches like online time series segmentation either use a fixed model or only apply an adaptive model on one dimensional data; it turns out that such methods are not very applicable to build online segmentation for multiple dimensional mobile sensor data (e.g., 3D accelerometer or 11 dimension features like 'mean', 'variance', 'covariance', 'magnitude', etc).In this paper, we design an adaptive model for segmenting real-time accelerometer data from smartphones, which is able to (a) dynamically select suitable dimensions to build a model, and (b) adaptively pick up a proper model. In addition to using the traditional residual-style regression errors to evaluate time series segmentation, we design a rich metric to evaluate mobile data segmentation results, including (1) traditional regression error, (2) information retrieval style measurements (i.e., precision, recall, F-measure), and (3) segmentation time delay.

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SeTraStream: Semantic-Aware Trajectory Construction over Streaming Movement Data

Cited by 30 publications

References 30 publications

A review of urban computing for mobile phone traces

A review of urban computing for mobile phone traces

Warped K-Means: An algorithm to cluster sequentially-distributed data

An adaptive approach for online segmentation of multi-dimensional mobile data

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