With the development and spread of Internet of Things (IoT) technology, various kinds of data are now being generated from IoT devices. Some data generated from IoT devices depend on geographical location and time, and we refer to them as spatio-temporal data (STD). Since the “locally produced and consumed” paradigm of STD use is effective for location-dependent applications, the authors have previously proposed a vehicle-based STD retention system. However, in low vehicle density environments, the data retention becomes difficult due to the decrease in the number of data transmissions in this method. In this paper, we propose a new data transmission control method for data retention in the low vehicle density environments.

Spatio-temporal dependent data, such as weather observation data, are data of which the attribute values depend on both time and space. Typical methods for the visualization of such data include plotting the attribute values at each point in time on a map and displaying series of the maps in chronological order with animation, or displaying them by juxtaposing horizontally or vertically. However, these methods are problematic in that they compel readers interested in grasping the spatial changes of the attribute values to memorize the representations on the maps. The problem is exacerbated by considering that the longer the time-period covered by the data, the higher the cognitive load. In order to solve these problems, the authors propose a visualization method capable of overlaying the representations of multiple instantaneous values on a single static map. This paper explains the design of the proposed method and reports two experiments conducted by the authors to investigate the usefulness of the method. The experimental results show that the proposed method is useful in terms of the speed and accuracy with which it reads the spatial changes and its ability to present data with long time series efficiently.

The GPS trajectory databases serve as bases for many intelligent applications that need to extract some trajectories for future processing or mining. When doing such tasks, spatio-temporal range queries based methods, which find all sub-trajectories within the given spatial extent and time interval, are commonly used. However, the history trajectory indexes of such methods suffer from two problems. First, temporal and spatial factors are not considered simutaneously, resulting in low performance when processing spatio-temporal queries. Second, the efficiency of indexes is sensitive to query size. The query performance changes dramatically as the query size changed. This paper proposes workload-aware Adaptive OcTree based Trajectory clustering Index (ATTI) aiming at optimizing trajectory storage and index performance. The contributions are three-folds. First, the distribution and time delay of the trajectory storage are introduced into the cost model of spatio-temporal range query; Second, the distribution of spatial division is dynamically adjusted based on GPS update workload; Third, the query workload adaptive mechanism is proposed based on virtual OcTree forest. A wide range of experiments are carried out over Microsoft GeoLife project dataset, and the results show that query delay of ATTI could be about 50% shorter than that of the nested index.

Link prediction is the task of inferring the existence or absence of certain relationships among data objects such as identity, interaction, and collaboration. Link prediction is found in various applications in the fields of information integration, recommender systems, bioinformatics, and social network analysis. The increasing interest in dynamically changing networks has led to growing interest in a more general link prediction problem called temporal link prediction in the data mining and machine learning communities. However, only links among nodes at the same time point are considered in temporal link prediction. We propose a new link prediction problem called cross-temporal link prediction in which the links among nodes at different time points are inferred. A typical example of cross-temporal link prediction is cross-temporal entity resolution to determine the identity of real entities represented by data objects observed in different time periods. In dynamic environments, the features of data change over time, making it difficult to identify cross-temporal links by directly comparing observed data. Other examples of cross-temporal links are asynchronous communications in social networks such as Facebook and Twitter, where a message is posted in reply to a previous message. We adopt a dimension reduction approach to cross-temporal link prediction; that is, data objects in different time frames are mapped into a common low-dimensional latent feature space, and the links are identified on the basis of the distance between the data objects. The proposed method uses different low-dimensional feature projections in different time frames, enabling it to adapt to changes in the latent features over time. Using multi-task learning, it jointly learns a set of feature projection matrices from the training data, given the assumption of temporal smoothness of the projections. The optimal solutions are obtained by solving a single generalized eigenvalue problem. Experiments using a real-world set of bibliographic data for cross-temporal entity resolution and a real-world set of emails for unobserved asynchronous communication inference showed that introducing time-dependent feature projections improved the accuracy of link prediction.

The increase in the advanced location based services such as traffic coordination and management necessitates the need for advanced models tracking the positions of Moving Objects (MOs) like vehicles. Due to computer processing limitations, it is impossible for MOs to continuously update their locations. This results in the uncertainty nature of a MO's location between any two reported positions. Efficiently managing and quantifying the uncertainty regions of MOs are needed in order to support different types of queries and to improve query response time. This challenging problem of modeling uncertainty regions associated with MO was recently addressed by researchers and resulted in models that ranged from linear which require few properties of MOs as input to the models, to non-linear that are able to more accurately represent uncertainty regions by considering higher degree input. This paper summarizes and discusses approaches in modeling uncertainty regions associated with MOs. It further illustrates the need for appropriate approximations especially in the case of non-linear models as the uncertainty regions become rather irregularly shaped and difficult to manage. Finally, we demonstrate through several experimental sets the advantage of non-linear models over linear models when the uncertainty regions of MOs are approximated by two different approximations; the Minimum Bounding Box (MBB) and the Tilted Minimum Bounding Box (TMBB).

Support of collaborative work and management of spatio-temporal data has become one of the most interesting and important database applications, which is due to the tremendous progress of database and its surrounding technologies in the last decade. In this paper, we investigate the new generation database technologies that are needed to support such advanced applications. Because of the recent progress of virtual reality technology, virtual work spaces are now available. We examine a typical CSCW (Computer Supported Cooperative Work) fsystem to identify database problems that arise from it. We introduce typical approaches to database improvement based on the high-level view and the virtual reality technique. Also, in this paper, the following are introduced and discussed: the design and implementation of three- and four-dimensional spatio-temporal database systems, VRML (Virtual Reality Modeling Language) database systems, fast access methods to spatio-temporal data, and the interval-based approach to temporal multimedia databases.

This paper describes a way of implementing a conceptual model for temporal data on a commercial object database system. The implemented version is provided as a class library. The library enables applications to handle temporal data. Any application can employ the library because it does not depend on specific applications. Furthermore, we propose an enhanced version of Time Index. The index efficiently processes event queries in particular. These queries search time intervals in which given events are all valid. We also investigate the effectiveness of the enhanced Time Index.

Object-Oriented database systems (OODBMS) are well known for modeling complex and dynamic application domains. Typically OODBMS have to handle large and complex structured objects whose values and structures can change frequently. Consequently there is a high demand for systems which support temporal and versioning features in both objects (or database population) and schema. This paper presents a mechanism for accessing the temporal versioned objects stored in the database which supports schema versioning. The results shown here can be considered as a value-added extension of our model called TVOO described in detail in [1] and [2]. In contrast to conventional database models, in TVOO objects and classes are not physically discarded from the database after they are modified or deleted. They are time dependent and the history of the changes which occur on them are kept as Version hierarchies. Therefore our model enriches the database environment with temporal and versioning features. Also, an access mechanism which makes it possible to access any object under any schema version is defined in such a way that not only objects created under old versions of schema classes can be accessed from new versions, but also objects created by new schema class versions can be accessed from old versions of the respective class.

This paper discusses an object-oriented approach to temporal multimedia data modeling in OMEGA; a multimedia database management under development at the University of Library and Information Science. An object-orientated approach is necessary to integrate various types of heterogeneous multimedia data, but it has become clear that current object-oriented data models are not sufficient to represent multimedia data, particularly when they are temporal. For instance, the current object-oriented data models cannot describe objects whose attribute values change time-dependently. Also, they cannot represent temporal relationships among temporal multimedia objects. We characterize temporal objects as instances of a subclass of class TimeInterval with the temporal attributes and the temporal relationships. This temporal multimedia data model is designed upward compatible with the ODMG-93 standard object model. To organize a temporal multimedia database, a five temporal axes model for representing temporal multimedia objects is also introduced. The five temporal axes--an absolute, an internal, a quasi-, a physical, and a presentation time axis--are necessary to describe time-dependent properties of multimedia objects in modeling, implementing and use. A concrete example of this organization method is also illustrated.