Analyses of temporal graphs provide valuable insights into temporal data through the use of two analytical approaches: temporal evolution and temporal information diffusion. The former shows how a network evolves over time; the latter explains how information spreads throughout a network over time. Systems have been mainly proposed to efficiently handle graph snapshots, which are suitable for temporal evolution but inappropriate for temporal information diffusion. For analyses of temporal information diffusion, temporal graph traversal platforms have recently been proposed; however, it is still infeasible to handle infinitely evolving temporal data, especially for monitoring applications. In this paper, we propose an incremental approach and its graph processing engine, Kairos, to enable prompt monitoring of temporal information diffusion. This approach makes it possible to immediately process diffusion results for sources of interest by traversing a part of the whole network, which avoids full traversals influenced by a small change in the network, thus making monitoring applications feasible. The recipes for implementing incremental versions of existing temporal graph traversal algorithms and metrics will make it easier for users to build their ad-hoc programs.

Analyses of temporal graphs provide valuable insights into temporal data through the use of two analytical approaches: temporal evolution and temporal information diffusion. The former shows how a network evolves over time; the latter explains how information spreads throughout a network over time. Systems have been mainly proposed to efficiently handle graph snapshots, which are suitable for temporal evolution but inappropriate for temporal information diffusion. For analyses of temporal information diffusion, temporal graph traversal platforms have recently been proposed; however, it is still infeasible to handle infinitely evolving temporal data, especially for monitoring applications. In this paper, we propose an incremental approach and its graph processing engine, Kairos, to enable prompt monitoring of temporal information diffusion. This approach makes it possible to immediately process diffusion results for sources of interest by traversing a part of the whole network, which avoids full traversals influenced by a small change in the network, thus making monitoring applications feasible. The recipes for implementing incremental versions of existing temporal graph traversal algorithms and metrics will make it easier for users to build their ad-hoc programs.

Temporal graph traversal is an approach for analyzing how information spreads throughout a network over time. A system has been recently proposed as an initial effort for efficient analyses against higher time complexity and infinitely evolving data unlike static graph. However, with the system, the response time for traversals from multiple sources is proportional to the number of sources; thus, application domains of the system can be limited. To resolve this problem, the state-of-the-art vertex-centric paradigm can be considered; however, we have found that the paradigm is not fitted into this computation. The paper proposes a novel time-centric computation approach for efficient all-pairs temporal graph traversals. One benefit of this approach is that users only need to focus on designing a repetitive task for graph elements that are valid at each sliding time, which simplifies the program logic and alleviates the burden of writing codes. Another benefit is that the approach is expected to enhance the performance by facilitating the reuse of intermediate results of multiple sources. The proposed approach is evaluated with a prototyped system, the recipes for existing algorithms, and the experiments with open temporal datasets. In addition, we also discuss how to handle ever-evolving real-world temporal networks.

Temporal graph traversal is an approach for analyzing how information spreads throughout a network over time. A system has been recently proposed as an initial effort for efficient analyses against higher time complexity and infinitely evolving data unlike static graph. However, with the system, the response time for traversals from multiple sources is proportional to the number of sources; thus, application domains of the system can be limited. To resolve this problem, the state-of-the-art vertex-centric paradigm can be considered; however, we have found that the paradigm is not fitted into this computation. The paper proposes a novel time-centric computation approach for efficient all-pairs temporal graph traversals. One benefit of this approach is that users only need to focus on designing a repetitive task for graph elements that are valid at each sliding time, which simplifies the program logic and alleviates the burden of writing codes. Another benefit is that the approach is expected to enhance the performance by facilitating the reuse of intermediate results of multiple sources. The proposed approach is evaluated with a prototyped system, the recipes for existing algorithms, and the experiments with open temporal datasets. In addition, we also discuss how to handle ever-evolving real-world temporal networks.

ChronoGraph is a novel system enabling temporal graph traversals. Compared to snapshot-oriented systems, this traversal-oriented system is suitable for analyzing information diffusion over time without violating a time constraint on temporal paths. The cornerstone of ChronoGraph aims at bridging the chasm between point-based semantics and period-based semantics and the gap between temporal graph traversals and static graph traversals. Therefore, our graph model and traversal language provide the temporal syntax for both semantics, and we present a method converting point-based semantics to period-based ones. Also, ChronoGraph exploits the temporal support and parallelism to handle the temporal degree, which explosively increases compared to static graphs. We demonstrate how three traversal recipes can be implemented on top of our system: temporal breadth-first search (tBFS), temporal depth-first search (tDFS), and temporal single source shortest path (tSSSP). According to our evaluation, our temporal support and parallelism enhance temporal graph traversals in terms of convenience and efficiency. Also, ChronoGraph outperforms existing property graph databases in terms of temporal graph traversals. We prototype ChronoGraph by extending Tinkerpop, a de facto standard for property graphs. Therefore, we expect that our system would be readily accessible to existing property graph users.

ChronoGraph is a novel system enabling temporal graph traversals. Compared to snapshot-oriented systems, this traversal-oriented system is suitable for analyzing information diffusion over time without violating a time constraint on temporal paths. The cornerstone of ChronoGraph aims at bridging the chasm between point-based semantics and period-based semantics and the gap between temporal graph traversals and static graph traversals. Therefore, our graph model and traversal language provide the temporal syntax for both semantics, and we present a method converting point-based semantics to period-based ones. Also, ChronoGraph exploits the temporal support and parallelism to handle the temporal degree, which explosively increases compared to static graphs. We demonstrate how three traversal recipes can be implemented on top of our system: temporal breadth-first search (tBFS), temporal depth-first search (tDFS), and temporal single source shortest path (tSSSP). According to our evaluation, our temporal support and parallelism enhance temporal graph traversals in terms of convenience and efficiency. Also, ChronoGraph outperforms existing property graph databases in terms of temporal graph traversals. We prototype ChronoGraph by extending Tinkerpop, a de facto standard for property graphs. Therefore, we expect that our system would be readily accessible to existing property graph users.

In recent years, the connected automotive industry has grown explosively. Various connected car services are emerging, such as remote vehicle diagnostics, driver's health monitoring, infotainment, and vehicle safety management. As a result, the number and type of vehicle data are increasing tremendously day by day. However, existing connected automotive solutions have a limitation in that each company manages its own closed data silos. This restricts connected car services from using data sources in various domains. Hence, we propose the GS1 Connected Car, an integrated vehicle information platform, and its ecosystem. We suggest GS1-based automotive data standards for not only in-vehicle data but also all the automotive-related data generated during the lifecycle of vehicles. We provide standardized data collection to EPCIS, the discovery of global automotive services using ONS, IoT Mash-up service between an in-car dashboard platform and IoT devices, video infotainment called GS1 video, and automotive lifecycle management application. We have implemented our platform in a real car by developing an Android-based vehicle dashboard, including service discovery, Mash-up services, and GS1 Video. Also, a mobile application for lifecycle management and Amazon skills for collecting driver's information are developed. Our demonstration and case study show the feasibility of the proposed platform, widening the scope of future connected car services.

In the EPCglobal standards for RFID architecture frameworks and interfaces, the Electronic Product Code Information System (EPCIS) acts as a standard repository storing event and master data that are well suited to Supply Chain Management (SCM) applications. Oliot-EPCIS broadens its scope to a wider range of IoT applications in a scalable and flexible way to store a large amount of heterogeneous data from a variety of sources. However, this expansion poses data security challenge for IoT applications including patients' ownership of events generated in mobile healthcare services. Thus, in this paper we propose Secure-EPCIS to deal with security issues of EPCIS for IoT applications. We have analyzed the requirements for Secure-EPCIS based on real-world scenarios and designed access control model accordingly. Moreover, we have conducted extensive performance comparisons between EPCIS and Secure-EPCIS in terms of response time and throughput, and provide the solution for performance degradation problem in Secure-EPCIS.