Mining GPS traces to recommend common meeting points
08 Aug 2012-pp 181-186
TL;DR: This paper presents a solution that identifies a common meeting point for a group of users who have temporal and spatial locality constraints that vary over time and uses daily movements information obtained from GPS traces for each user to compute stay points during various times of the day.
Abstract: Scheduling a meeting is a difficult task for people who have overbooked calendars and many constraints. The complexity increases when the meeting is to be scheduled between parties who are situated in geographically distant locations of a city and have varying travel patterns. In this paper, we present a solution that identifies a common meeting point for a group of users who have temporal and spatial locality constraints that vary over time. The problem entails answering an Optimal Meeting Point (OMP) query in spatial databases. Under Euclidean space OMP query solution identification gets reduced to the problem of determining the geometric median of a set of points, a problem for which no exact solution exists. The OMP problem does not consider any constraints as far as availability of users is concerned whereas that is a key constraint in our setting. We therefore focus on finding a solution that uses daily movements information obtained from GPS traces for each user to compute stay points during various times of the day. We then determine interesting locations by analyzing the stay points across multiple users. The novelty of our solution is that the computations are done within the database by using various relational algebra operations in combination with statistical operations on the GPS trajectory data. This makes our solution scalable to larger groups of users and for multiple such requests. Once this list of stay points and interesting locations are obtained, we show that this data can be utilized to construct spatio-temporal graphs for the users that allow us efficiently decide a meeting place. We perform experiments on a real-world dataset and show that our method is effective in finding an optimal meeting point between two users.
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01 Jan 2013
TL;DR: This paper focuses on the conceptual benefits and risks such an integration of sensor data into social media in the case of a patient room and introduces a way to deal with these problems.
Abstract: In a hospital, information exchange is essential to save lives and to prevent life-endangering mistakes. Information exchange is supported by a hospital information system (HIS). From a theoretical perspective, the deployment of an HIS is promising because it reduces errors and duplication of information. In practice, however, there are some major problems concerning the usage of such a system. One way to deal with these problems is introduced in this paper: the integration of sensor data into social media. The paper concentrates on the conceptual benefits and risks such an integration may generate. It focuses on the case of a patient room.
47 citations
27 Jun 2015
TL;DR: Dart provides a hybrid table schema to store spatial data in HBase so that the Reduce process can be omitted for operations like calculating the mean center and the median center and it also supports massive spatial data analysis like K-Nearest Neighbors and Geometric Median Distribution.
Abstract: In the field of big data research, analytics on spatio-temporal data from social media is one of the fastest growing areas and poses a major challenge on research and application. An efficient and flexible computing and storage platform is needed for users to analyze spatio-temporal patterns in huge amount of social media data. This paper introduces a scalable and distributed geographic information system, called Dart, based on Hadoop and HBase. Dart provides a hybrid table schema to store spatial data in HBase so that the Reduce process can be omitted for operations like calculating the mean center and the median center. It employs reasonable pre-splitting and hash techniques to avoid data imbalance and hot region problems. It also supports massive spatial data analysis like K-Nearest Neighbors (KNN) and Geometric Median Distribution. In our experiments, we evaluate the performance of Dart by processing 160 GB Twitter data on an Amazon EC2 cluster. The experimental results show that Dart is very scalable and efficient.
20 citations
TL;DR: Two algorithms for the pre-processing of GPS data in order to deal with outlier identification and missing data imputation; a clustering approach to recover the main points of interest from GPS trajectories; and a weighted-directed network, which incorporates the most relevant characteristics of the GPS trajectory at an aggregate level are proposed.
Abstract: Global Positioning System (GPS) devices afford the opportunity to collect accurate data on unit movements from temporal and spatial perspectives With a special focus on GPS technology in travel su
18 citations
Cites methods from "Mining GPS traces to recommend comm..."
...This approach has been undertaken to classify and rank places (Tiwari & Kaushik, 2013), evaluate the shared characteristics of users (Andrade &Gama, 2019), and even for the determination of optimal meeting points for a set of users (Khetarpaul et al., 2012)....
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02 Sep 2013
TL;DR: This work extends the work of previous authors in this domain by incorporating some real life constraints (varying travel patterns, flexible meeting point and considering road network distance) and generalizes the problem by considering variable number of users.
Abstract: Scheduling a meeting is a difficult task for people who have overbooked calendars and many constraints. This activity becomes further complex when the meeting is to be scheduled between parties who are situated in geographically distant locations of a city and have varying traveling patterns. We extend the work of previous authors in this domain by incorporating some real life constraints (varying travel patterns, flexible meeting point and considering road network distance). We also generalize the problem by considering variable number of users. The previous work does not consider these dimensions. The search space for optimal meeting point is reduced by considering convex hull of the set of users locations. It can be further pruned by considering other factors, e.g., direction of movement of users. Experiments are performed on a real-world dataset and show that our method is effective in stated conditions.
4 citations
Cites background from "Mining GPS traces to recommend comm..."
...In our previous work [1] we have determined common meeting point from spatiotemporal graph analysis for two users in the Euclidean space....
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Dissertation•
01 Dec 2016
TL;DR: Communicating Meaning in Context-Aware System Design: Talking about meaning in context-aware systems design helps clarify meaning in systems design.
Abstract: Communicating Meaning in Context-Aware System Design
2 citations
Cites background from "Mining GPS traces to recommend comm..."
...Other work has sought to provide recommendations to users for places to meet by using overlap in GPS coordinates (Khetarpaul & Gupta, 2012)....
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References
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20 Apr 2009
TL;DR: This work first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG), and proposes a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location.
Abstract: The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we aim to mine interesting locations and classical travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG). Second, based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location. This model infers the interest of a location by taking into account the following three factors. 1) The interest of a location depends on not only the number of users visiting this location but also these users' travel experiences. 2) Users' travel experiences and location interests have a mutual reinforcement relationship. 3) The interest of a location and the travel experience of a user are relative values and are region-related. Third, we mine the classical travel sequences among locations considering the interests of these locations and users' travel experiences. We evaluated our system using a large GPS dataset collected by 107 users over a period of one year in the real world. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, when considering the users' travel experiences and location interests, we achieved a better performance beyond baselines, such as rank-by-count and rank-by-interest, etc.
1,903 citations
"Mining GPS traces to recommend comm..." refers background or methods in this paper
...Mobile tourist guide systems [7, 6, 8] typically recommend locations and sometimes provide navigation information based on a user’s real-time location....
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...GPS trajectory dataset [8] was collected by Microsoft Research....
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21 Sep 2008
TL;DR: An approach based on supervised learning to infer people's motion modes from their GPS logs is proposed, which identifies a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used.
Abstract: Both recognizing human behavior and understanding a user's mobility from sensor data are critical issues in ubiquitous computing systems As a kind of user behavior, the transportation modes, such as walking, driving, etc, that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs The contribution of this work lies in the following two aspects On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement
1,054 citations
"Mining GPS traces to recommend comm..." refers background in this paper
...length() do if DistanceNodes[k][2] = 0 then if TimeDiff(DistanceNodes[i][0], DistanceNodes[i][1]) ≤ ThreshTime then DistanceNodes[k][3]← Rank + 1...
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...DistanceNodes[k][2]/2 if T imeDiff(DistanceNodes[i][0], DistanceNodes[i][1]) = T hreshT ime then DistanceNodes[k][3] ....
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...else if DistanceNodes[k][2] 6= 0 then DistanceNodes[k][2]← DistanceNodes[k][2]/2 if TimeDiff(DistanceNodes[i][0], DistanceNodes[i][1]) ≤ ThreshTime then DistanceNodes[k][3]← Rank + 1...
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...GeoLife enables users to share travel experiences using GPS trajectories [2, 3, 4]....
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...Rank +1 else if DistanceNodes[k][2] =0 then DistanceNodes[k][2] ....
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17 Sep 2006
TL;DR: A method called Predestination is described that uses a history of a driver's destinations, along with data about driving behaviors, to predict where a driver is going as a trip progresses, to produce a probabilistic map of destinations.
Abstract: We describe a method called Predestination that uses a history of a driver's destinations, along with data about driving behaviors, to predict where a driver is going as a trip progresses. Driving behaviors include types of destinations, driving efficiency, and trip times. Beyond considering previously visited destinations, Predestination leverages an open-world modeling methodology that considers the likelihood of users visiting previously unobserved locations based on trends in the data and on the background properties of locations. This allows our algorithm to smoothly transition between “out of the box” with no training data to more fully trained with increasing numbers of observations. Multiple components of the analysis are fused via Bayesian inference to produce a probabilistic map of destinations. Our algorithm was trained and tested on hold-out data drawn from a database of GPS driving data gathered from 169 different subjects who drove 7,335 different trips.
546 citations
"Mining GPS traces to recommend comm..." refers methods in this paper
...Other systems use inference from Partial Trajectories [11] that use the history of a driver s destinations, along with data about driving behaviors, to predict where a driver is going as a trip progresses....
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...Other systems use inference from Partial Trajectories [11] that use...
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11 Jul 2007
TL;DR: A map-based personalized recommendation system which reflects user's preference modeled by Bayesian Networks (BN) and infers the most preferred item to provide an appropriate service by displaying onto the mini map.
Abstract: As wireless communication advances, research on location-based services using mobile devices has attracted interest, which provides information and services related to user's physical location. As increasing information and services, it becomes difficult to find a proper service that reflects the individual preference at proper time. Due to the small screen of mobile devices and insufficiency of resources, personalized services and convenient user interface might be useful. In this paper, we propose a map-based personalized recommendation system which reflects user's preference modeled by Bayesian Networks (BN). The structure of BN is built by an expert while the parameter is learned from the dataset. The proposed system collects context information, location, time, weather, and user request from the mobile device and infers the most preferred item to provide an appropriate service by displaying onto the mini map.
437 citations
"Mining GPS traces to recommend comm..." refers background in this paper
...Mobile tourist guide systems [7, 6, 8] typically recommend locations and sometimes provide navigation information based on a user’s real-time location....
[...]
22 Aug 2004
TL;DR: This work defines the spatiotemporal periodic pattern mining problem and proposes an effective and fast mining algorithm for retrieving maximal periodic patterns, and devise a novel, specialized index structure that can benefit from the discovered patterns to support more efficient execution of spatiotsemporal queries.
Abstract: In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. For example, people wake up at the same time and follow more or less the same route to their work everyday. The discovery of hidden periodic patterns in spatiotemporal data, apart from unveiling important information to the data analyst, can facilitate data management substantially. Based on this observation, we propose a framework that analyzes, manages, and queries object movements that follow such patterns. We define the spatiotemporal periodic pattern mining problem and propose an effective and fast mining algorithm for retrieving maximal periodic patterns. We also devise a novel, specialized index structure that can benefit from the discovered patterns to support more efficient execution of spatiotemporal queries. We evaluate our methods experimentally using datasets with object trajectories that exhibit periodicity.
312 citations
"Mining GPS traces to recommend comm..." refers methods in this paper
...Other methods include mining, indexing and querying historical spatio-temporal Data that work under the assumption that the objects follow the same routes (approximately) over regular time intervals [9]....
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