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navigation systems, allow users to record their location history. The location history data can be analyzed to generate life patterns|patterns that associate people to places they frequently visit. Accordingly, an SSN is a graph that consists of (1) a social network, (2) a spatial network, and (3) life patterns that connect the users of the social...
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... coworkers are people who arrive at the power plant five days a week (Monday to Friday, represented as 2,3,4,5,6) and stay there from 8 until 18, with confidence 0.6. Finally, we intersect the neighbors and the coworkers to find potential people for the carpooling. Figure 3 illustrates the formulation of this query in the sys- A video that presents the system can be viewed online at . tem. Figure 4 illustrates the result when evaluating the query over the dataset of the demo. The answer to a query can either be geographical entities or a list of people from the social network. Figure 5 illustrates an answer to a query that returns a list of people. The results can be examined by zooming in and zooming out, and colors are being used to indicate the relevancy level of entities—highly relevant entities are colored by red, and the color changes from red to blue as relevancy decreases (see the bar at the bottom of the map). By clicking the nodes of the expression tree of a query, the results of the subexpressions are presented. This is used for analyzing the results and it assists in query optimization and in ...
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We present Etymo (https://etymo.io), a discovery engine to facilitate artificial intelligence (AI) research and development. It aims to help readers navigate a large number of AI-related papers published every week by using a novel form of search that finds relevant papers and displays related papers in a graphical interface. Etymo constructs and m...
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... One type of conflict exists between the value of the security of contributor's personal information and privacy of the individual and the value of the usability and availability of the data provided by that person. Nowadays, it is possible to identify and expose private information about the volunteers based on the spatial data and information they provide, e.g. a person's financial status, lifestyle, and health (Doytsher, Galon, and Kanza 2012). In Ahmouda, Hochmair and Cvetojevic (2018), patterns of OSM contributors and Twitter users were used to explore whether a contributor is local or non-local. ...
The crowdsourced OpenStreetMap mapping platform is utilized by countless stakeholders worldwide for various purposes and applications. Individuals, researchers, governments, commercial, and humanitarian organizations, in addition to the engineers, professionals, and technical developers, use OpenStreetMap both as data contributors and consumers. The storage, usage, and integration of volunteered geographical data in software applications often create complex ethical dilemmas and values regarding the relationships between different categories of stakeholders. It is therefore common for moral preferences of stakeholders to be neglected. This paper investigates the integration of ethical values in OpenStreetMap using the value sensitive design methodology that examines technical, empirical, and conceptual aspects at each design stage. We use the Humanitarian OpenStreetMap Team, an existing volunteered geographic information initiative, as a case study. Our investigation shows that although OpenStreetMap does integrate ethical values in its organizational structure, a deeper understanding of its direct and indirect stakeholders’ perspectives is still required. This study is expected to assist organizations that contribute to or use OpenStreetMap in recognizing and preserving existing and important ethical values. To the best of our knowledge, this is the first attempt to evaluate ethical values methodically and comprehensively in the design process of the OpenStreetMap platform.
... In datacentric sense, a social network consists of a virtual social graph where users (nodes) are connected through relationships (edges) (Johnson et al. 2011). In a sociospatial network, nodes (and sometimes edges) on a graph are encoded with geographic information that offers the possibility of linking social interactions with the geographic context(s) in which they occur (Doytsher et al. 2012, Sarkar et al. 2016. Many studies have demonstrated the importance of online SSN data from platforms such as Facebook, Twitter and Instagram for analysis of activity spaces Kwan 2011, Wang et al. 2012), health and places (Cravey et al. 2001, Thomas 2016, and crisis/disaster response, among others (Comfort et al. 2004, De Albuquerque et al. 2015. ...
Despite several high-profile data breaches and business models that commercialize user data, participation in social media networks continues to require users to trust corporations to safeguard their personal data. Since these data increasingly contain geographic references that allude to individuals’ locations and movements, the need for new approaches to geoprivacy and data sovereignty has grown. We develop a geoprivacy framework that couples two emerging technologies – decentralized data storage and discrete global grid systems – to facilitate fine-grained user control over the ownership of, access to and map-based representation of their data. The framework is illustrated with a dynamic k-anonymity model that links the geographic precision of shared data to social trust within in a social network. In this framework, users’ spatio-temporal data are shared through a decentralized system and are represented on a discrete global grid data model at spatial resolutions that correspond to varying degrees of trust between individuals who are exchanging information. Our framework has several advantages over centralized geoprivacy approaches, namely trust in a third-party entity is not required and geoprivacy is dynamic and context-dependent with users maintaining autonomy. As the distributed web begins to emerge, so too can the next generation of geographic information sharing tools.
... Note that GraSp is not a new query type. In fact, there has been a flurry of recent research papers that tackle a variety of GraSp applications [1,2,5,6,11,15,17,19]. For instance, the research work about Range Friends and Nearest Friends queries find friends of a user within a given distance and the user's top-K nearest friends, respectively [1]. ...
The paper proposes Riso-Tree, a generic indexing framework for geographic knowledge graphs. Riso-Tree enables fast execution of graph queries that involves spatial predicates (aka. GraSp). The proposed framework augments the classic R-Tree structure with pre-materialized sub-graph entries. Riso-Tree first partitions the graph into sub-graphs based on their connectivity to the spatial sub-regions. The proposed index allows for fast execution of GraSp queries by efficiently pruning the traversed vertexes/edges based upon the materialized sub-graph information. The experiments show that the proposed Riso-Tree achieves up to two orders magnitude faster execution time than its counterparts when executing GraSp queries on real knowledge graphs (e.g., Wikidata).
... For example, Ye et al. [76] showed how to extract life patterns from the location history of people, that is, finding spatio-temporal patterns that associate the users to geographic locations they regularly visit. Doytsher et al. [24] showed how to use life patterns to associate users of a social network with places these users frequently visit, and how to query the combined data [26]. In such cases, the search can use the two aspects of the data, e.g., find people who frequently visit a certain type of places, or find places that certain types of people frequently visit. ...
... The need to collect, query and use geo-social data has been recognized in recent years and there is a growing interest in utilizing such data. Querying geo-social data [21,24,26] and using data to understand user preferences, in recommendation systems [9,25,34] were studied. Karamshuk et al. [46] showed how to find an optimal location for a new retail store, based on geo-social data. ...
Geo-social data sets, which fuse the social and the geospatial facets of data, are vibrant data sources that associate people and activities with locations. In a combined geo-social search, several search queries are posed over geospatial and social data sources, or over data sources with both geospatial and social facets; and the search results, provided as ranked lists of items, are integrated by associating matching items, yielding combinations. Each combination has a score which is a function of the scores of the items it comprises, and the goal is to compute the k combinations with the highest score, that is, the top-k combinations. However, since geo-social data sources are heterogeneous, data items may not have matching items in all the ranked lists. Such items cannot be included in complete combinations. Hence, we study the approach where combinations are padded by nulls for missing items, as in outer-join. A combination is maximal if it cannot be extended by replacing a null by an item. We show that if some of the top-k maximal combinations contain null values, the computation requires reading entire lists, and hence, traditional top-k algorithms and optimization techniques are not as effective as in the case of an ordinary top-k join. Thus, we present two novel algorithms for computing the top-k maximal combinations. One novel algorithm is instance optimal over the class of algorithms that compute a 𝜃-approximation to the answer. The second algorithm is more efficient than the modification of two common top-k algorithms to compute maximal combinations. We show this analytically, and experimentally over real and synthetic data.
... GeoSocial Graph. GeoSocial graphs have been extensively studied in the literature [2], [19], [7], [8], [15], [21], [1]. Geospatial data are integrated into the social graph in different ways. ...
... Geospatial data are integrated into the social graph in different ways. In [7], [8], a GeoSocial graph is defined as an combination of social graph data and road network data. The two network structures are combined according to visiting history of people in the social graph. ...
... The need to collect, query and use geosocial data has been recognized in recent years and there is a growing interest in utilizing such data. Querying geosocial data [7,9,11] and using data to understand user preferences, in recommendation systems [3,8,10] were studied. Elbery et al. [12] studied the use of geosocial data for carpooling. ...
When recording their GPS trajectories or posting geo-tagged content on social networks, people produce social spatio-temporal data that can be stored and shared, namely geosocial data. Much of these spatio-temporal data can be used by organizations and applications, for statistical analysis or to provide services that are based on data. By letting people sell the data they produce, to different consumers, both sides can benefit. Thus, we present here a visionary idea of a geosocial marketplace where people and organizations can sell, buy and exchange geosocial data, that is, trade with spatio-temporal data pertaining people. We discuss the involved challenges, such as how to define supply and demand, pricing data, privacy issues and measuring the amount of data being exchanged. We explain the importance of the approach and its applicability. We believe that the proposed vision could motivate followup research in the area of sharing and exchanging spatio-temporal data as well as determining appropriate price points.
... For example, Cheng et al. [Cheng et al. 2011] analyzed 22 million check-ins posted on more than 1,200 applications (Foursquare is responsible for 53.5% of the total). They observed that users follow simple and reproducible patterns, 10 and also that social status as well as geographic and economic factors are coupled with mobility. Scellato et al. [Scellato et al. 2011] studied the spatial properties of the social networks connecting users of Foursquare, Gowalla, and Brightkite. ...
... More closely related to our work, Doytsher et al. [Doytsher et al. 2012] proposed an application that handles a social-spatial network consisting of a social network, 30 a spatial network, and life patterns that connect users of the social network to the locations where they regularly go. In the application, users can create queries such as "friends of Marge who buy at the same grocery store that she does", using a new query language. ...
... From a participatory sensing system one can derive a participatory sensor network (PSN) [Silva et al. 2013b] where the users portable devices are the fundamental building block. Individuals carrying their devices are able to sense the environment they are 10 inserted in and make relevant observations at a personal level. Thus, each node in a PSN consists of the user plus his/her mobile device. ...
We here investigate the potential of participatory sensor networks derived from location sharing systems, such as Foursquare, to understand the human dynamics of cities. We propose the City Image visualization technique, which builds a transition graph mapping people's movements between location categories, and demonstrate its use to identify similarities and differences of human dynamics across cities by clustering cities according to their citizens' routines. We also analyze centrality metrics of the transition graphs built for different cities, considering transitions between specific venues. We show that these metrics complement the City Image technique, contributing to a deeper understanding of city dynamics.
... (De Smith et al., 2011). GIS (Doytsher et al., 2012). ...
... . Ahn andJoo(2011), Jung(2013) LBSNS .Cranshaw et al.(2010),Doytsher et al.(2012), Cypher (Neo4j, 2013). id id (Fig. 3). . ...
In general, topographical phenomena are represented graphically by data in the spatial domain, while attributes of the non-spatial domain are expressed by alpha-numeric texts. GIS functions for analysis of attributes in the non-spatial domain remain quite simple, such as search methods and simple statistical analysis. Recently, graph modeling and network analysis of social phenomena are commonly used for understanding various social events and phenomena. In this study, we applied the network analysis functions to the non-spatial domain data of GIS to enhance the overall spatial analysis. For this purpose, a novel design was presented to integrate the spatial database and the graph database, and this design was then implemented into a WebGIS system for better decision makings. The developed WebGIS with underlying synchronized databases, was tested in a simulated application about the selection of water supply households during an epidemic of the foot-and-mouse disease. The results of this test indicate that the developed WebGIS can contribute to improved decisions by taking into account the social proximity factors as well as geospatial factors.
... Graph databases are a suitable choice for the following types of applications: location-based services, recommendation engines, and complex network-based applications including social, information, technological, and biological networks [15,66]. For instance, user location history data which are used to generate patterns that associate people with their frequently visited places could be efficiently stored and queried using Neo4J in location-based sociospatial network applications [67]. Similarly, recommendation-based systems in which users are provided directed content based on their preferences could be efficiently built using graph databases. ...
Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the large number and diversity of existing NoSQL and NewSQL solutions, it is difficult to comprehend the domain and even more challenging to choose an appropriate solution for a specific task. Therefore, this paper reviews NoSQL and NewSQL solutions with the objective of: (1) providing a perspective in the field, (2) providing guidance to practitioners and researchers to choose the appropriate data store, and (3) identifying challenges and opportunities in the field. Specifically, the most prominent solutions are compared focusing on data models, querying, scaling, and security related capabilities. Features driving the ability to scale read requests and write requests, or scaling data storage are investigated, in particular partitioning, replication, consistency, and concurrency control. Furthermore, use cases and scenarios in which NoSQL and NewSQL data stores have been used are discussed and the suitability of various solutions for different sets of applications is examined. Consequently, this study has identified challenges in the field, including the immense diversity and inconsistency of terminologies, limited documentation, sparse comparison and benchmarking criteria, and nonexistence of standardized query languages.
... Things (or objects) in the Internet of Things [1,2] can be modeled as MBRs [3] in some context, and spatial index and query are enabling techniques for searching objects that a user interests. K-neighbor query method is widely used for querying spatial objects in spatial database. ...
Spatial index and query are enabling techniques for achieving the vision of the Internet of Things. K-NN is an algorithm which is used widely in spatial database. Traditional query algorithms use R-tree as the index structure and improve the query efficiency by using the measurement distance and pruning strategy. Based on the study of previous algorithms, this paper proposes a novel K-NN query algorithm based on PB-tree with the parallel lines division. PB-tree index is different from the traditional R-tree index, where PB-tree adopts parallel lines to divide the spatial region and uses parallel lines as the parent node. It is similar to the binary tree index structure and requires to query three small portions nearest to the queried object in each K-NN query. Therefore, the search range is narrowed and the query efficiency is enhanced. Experiments show that PB-tree is better than the traditional R-tree from the aspect of query performance. PB-tree can avoid the deficiency of a large number of overlap and coverage among odes in R-tree and multiple index paths when searching data objects, and hence PB-tree can find K-NN objects meeting the conditions quickly and efficiently in large data sets.
