Spatiotemporal social (STS) data model: correlating social networks and spatiotemporal data
TL;DR: A STS data model is proposed which captures both non-spatial and spatial properties of moving users, connected on social network and extends spatiotemporal data model for moving objects proposed in Ferreira et al. (Trans GIS 18(2):253–269, 2014).
Abstract: A location-based social network is a network representation of social relations among actors, which not only allow them to connect to other users/friends but also they can share and access their physical locations. Here, the physical location consists of the instant location of an individual at a given timestamp and the location history that an individual has accumulated in a certain period. This paper aimed to capture this spatiotemporal social network (STS) data of location-based social networks and model it. In this paper, we propose a STS data model which captures both non-spatial and spatial properties of moving users, connected on social network. In our model, we define data types and operations that make querying spatiotemporal social network data easy and efficient. We extend spatiotemporal data model for moving objects proposed in Ferreira et al. (Trans GIS 18(2):253–269, 2014) for social networks. The data model infers individual’s location history and helps in querying social network users for their spatiotemporal locations, social links, influences, their common interests, behavior, activities, etc. We show the some results of applying our data model on a spatiotemporal dataset (GeoLife) and two large real-life spatiotemporal social network datasets (Gowalla, Brightkite) collected over a period of two years. We apply the proposed model to determine interesting locations in the city and correlate the impact of social network relationships on the spatiotemporal behavior of the users.
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TL;DR: A model named social strength prediction model is proposed, which infers social connections among smart objects and predicts the strength of the connections using the co-usage data of the objects, and is capable of inferring social connections and predicting their corresponding social strength with relatively high precision and recall.
Abstract: Recently, the emerging Social Internet of Things (SIoT) has opened up a myriad of research opportunities One of the most fundamental research challenges posed by SIoT and its objective is to allow smart objects to create and maintain their own social networks In this paper, we are interested in constructing these social networks, which are built upon social relationships between smart objects, and quantitatively estimating the social strength values of the relations In particular, we propose a model named social strength prediction model, which infers social connections among smart objects and predicts the strength of the connections using the co-usage data of the objects The proposed model is divided into two major components: 1) entropy-based and 2) distance-based social strength computations The two components capture different properties of co-usages of objects, namely, the diversity and spatiotemporal features, which are all essential factors that contribute to the values of social strength In order to test the feasibility of the proposed model, we conducted extensive sets of experiments with real-world datasets, which include the history data of various object usages Based on the results, we show that the proposed model is in fact capable of inferring social connections and predicting their corresponding social strength with relatively high precision and recall
22 citations
Cites background from "Spatiotemporal social (STS) data mo..."
...This paradigm has indeed produced tremendous business and social opportunities with a wide applicability in many productive sectors [3], [4]....
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07 Dec 2020
TL;DR: In this paper, the authors proposed an efficient time-aware machine learning-driven trust evaluation model to address the problem of establishing trustworthy relationships and building trust among objects in the social Internet of Things (SIoT).
Abstract: The emerging paradigm of the Social Internet of Things (SIoT) has transformed the traditional notion of the Internet of Things (IoT) into a social network of billions of interconnected smart objects by integrating social networking facets into the same. In SIoT, objects can establish social relationships in an autonomous manner and interact with the other objects in the network based on their social behaviour. A fundamental problem that needs attention is establishing of these relationships in a reliable and trusted way, i.e., establishing trustworthy relationships and building trust amongst objects. In addition, it is also indispensable to ascertain and predict an object’s behaviour in the SIoT network over a period of time. Accordingly, in this paper, we have proposed an efficient time-aware machine learning-driven trust evaluation model to address this particular issue. The envisaged model deliberates social relationships in terms of friendship and community-interest, and further takes into consideration the working relationships and cooperativeness (object-object interactions) as trust parameters to quantify the trustworthiness of an object. Subsequently, in contrast to the traditional weighted sum heuristics, a machine learning-driven aggregation scheme is delineated to synthesize these trust parameters to ascertain a single trust score. The experimental results demonstrate that the proposed model can efficiently segregates the trustworthy and untrustworthy objects within a network, and further provides the insight on how the trust of an object varies with time along with depicting the effect of each trust parameter on a trust score.
13 citations
TL;DR: This study defines SSTILs and SSTil mining problem by taking into account spatial, temporal, and social dimensions of the social media datasets, and proposes methods and interest measures to discover S STILs efficiently based on both user and group preferences.
12 citations
TL;DR: In this article, the impact of structural patterns hidden in the nodes of a friendship network and external environment changes on the check-in patterns of the users is analyzed. And the collective behavior of the all the users during some special events is mined.
Abstract: Analyzing and understanding the movement patterns of the citizen’s with in a city, plays an important role in urban and transportation planning. Though many recent research papers focused on mining LBSN services data and performed in-depth analysis of users’ mobility patterns and their impact on their social inter-connections and friends. This paper focuses on understanding the Citizen’s movement patterns of socially interconnected users in friendship networks, by analyzing their spatial-temporal footprints/check-ins. The aim of this paper is to find the impact of structural patterns hidden in the nodes of a friendship network and external environment changes on the check-in patterns of the users. First, we classify each spatial check-in event based on its cause into either self reinforcing behavior or social influence or external stimulus. Then we mine the collective behavior of the all the users during some special events.
1 citations
TL;DR: A framework that combines social, geographical, and temporal information for a relevance model centered around the use of semantic annotations on Points of Interest with the goal of addressing recreational queries in information retrieval is proposed.
Abstract: Recreational queries from users searching for places to go and things to do or see are very common in web and mobile search. Users specify constraints for what they are looking for, like suitability for kids, romantic ambiance or budget. Queries like “restaurants in New York City” are currently served by static local results or the thumbnail carousel. More complex queries like “things to do in San Francisco with kids” or “romantic places to eat in Seattle” require the user to click on every element of the search engine result page to read articles from Yelp, TripAdvisor, or WikiTravel to satisfy their needs. Location data, which is an essential part of web search, is even more prevalent with location-based social networks and offers new opportunities for many ways of satisfying information seeking scenarios. In this paper, we address the problem of recreational queries in information retrieval and propose a solution that combines search query logs with LBSNs data to match user needs and possible options. At the core of our solution is a framework that combines social, geographical, and temporal information for a relevance model centered around the use of semantic annotations on Points of Interest with the goal of addressing these recreational queries. A central part of the framework is a taxonomy derived from behavioral data that drives the modeling and user experience. We also describe in detail the complexity of assessing and evaluating Point of Interest data, a topic that is usually not covered in related work, and propose task design alternatives that work well. We demonstrate the feasibility and scalability of our methods using a data set of 1B check-ins and a large sample of queries from the real-world. Finally, we describe the integration of our techniques in a commercial search engine.
1 citations
References
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21 Aug 2011
TL;DR: A model of human mobility that combines periodic short range movements with travel due to the social network structure is developed and it is shown that this model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance.
Abstract: Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.
2,922 citations
"Spatiotemporal social (STS) data mo..." refers background in this paper
...Some researchers have analyzed social network information to infer user location, in Gao et al. (2012), Sadilek et al. (2012). Authors in Backstrom et al....
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...Some researchers have analyzed social network information to infer user location, in Gao et al. (2012), Sadilek et al....
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...Cho et al. (2011) explore human spatiotemporal movement in relation to social ties to analyze future check-ins of a user and effects of distance between users on future check-ins in a typical social network....
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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
"Spatiotemporal social (STS) data mo..." refers methods in this paper
...…many algorithms, techniques and languages (Erwig et al. 1999; Galton and Worboys 2005; Güting and Schneider 2005; Khetarpaul et al. 2011, 2013; Worboys and Hornsby 2004; Zheng et al. 2008, 2009; Zheng 2015) have been proposed to detect patterns of trajectories and mine meaningful information....
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...GPS trajectory dataset (GeoLife) (Zheng et al. 2008, 2009; Zheng 2015) was collected by Microsoft Research....
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TL;DR: A systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics, and introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors.
Abstract: The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.
1,289 citations
"Spatiotemporal social (STS) data mo..." refers methods in this paper
...…many algorithms, techniques and languages (Erwig et al. 1999; Galton and Worboys 2005; Güting and Schneider 2005; Khetarpaul et al. 2011, 2013; Worboys and Hornsby 2004; Zheng et al. 2008, 2009; Zheng 2015) have been proposed to detect patterns of trajectories and mine meaningful information....
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...In the spatiotemporal data mining research area, many algorithms, techniques and languages (Erwig et al. 1999; Galton and Worboys 2005; Güting and Schneider 2005; Khetarpaul et al. 2011, 2013; Worboys and Hornsby 2004; Zheng et al. 2008, 2009; Zheng 2015) have been proposed to detect patterns of trajectories and mine meaningful information....
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...GPS trajectory dataset (GeoLife) (Zheng et al. 2008, 2009; Zheng 2015) 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
"Spatiotemporal social (STS) data mo..." refers methods in this paper
...…many algorithms, techniques and languages (Erwig et al. 1999; Galton and Worboys 2005; Güting and Schneider 2005; Khetarpaul et al. 2011, 2013; Worboys and Hornsby 2004; Zheng et al. 2008, 2009; Zheng 2015) have been proposed to detect patterns of trajectories and mine meaningful information....
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...GPS trajectory dataset (GeoLife) (Zheng et al. 2008, 2009; Zheng 2015) was collected by Microsoft Research....
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Book•
01 May 2014TL;DR: This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics.
Abstract: The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike. Key features: Covers both core methods and cutting-edge research Algorithmic approach with open-source implementations Minimal prerequisites: all key mathematical concepts are presented, as is the intuition behind the formulas Short, self-contained chapters with class-tested examples and exercises allow for flexibility in designing a course and for easy reference Supplementary website with lecture slides, videos, project ideas, and more
844 citations