The DBLP Computer Science Bibliography evolved from an early small experimental Web server to a popular service for the computer science community. Many design decisions and details of the public XML-records behind DBLP never were documented. This paper is a review of the evolution of DBLP. The main perspective is data modeling. In DBLP persons play a central role, our discussion of person names may be applicable to many other data bases. All DBLP data are available for your own experiments. You may either download the complete set, or use a simple XML-based API described in an online appendix.

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DBLP: some lessons learned

Review: Service-oriented middleware: A survey

In the era of smart devices and connected neighborhoods, the ubiquitous monitoring and care of patients are possible with the Internet of Medical Things (IoMT). Smart healthcare devices may serve their purpose well when they are able to share patient's data with each other. However, data formats vary widely across vendors, rendering these devices not interoperable. Recent solutions mostly rely on cloud services where a source device uploads the data, and the sink devices download it conforming to their own native formats. However, the quality of service is expected to deteriorate in a cloud processing regime with inherent network delays and traffic congestion, and the real-time data acquisition and manipulation is, therefore, not possible. This article presents MeDIC, a framework of Medical Data Interoperability through Collaboration of healthcare devices. MeDIC improves over a cloud-based IoMT by utilizing translation resources at the network edge, with its probing and translating agents. The probing agents maintain a capability list of MeDIC devices within a local network and enable one MeDIC device to request data conversion from another device when the former is not capable of this conversion by itself. The translating agent of the later then converts the data into the required format and returns it to the former. These novel agents allow IoMT devices to share their redundant computing resources for data translations in order to minimize cloud accesses. Legacy devices are supported through MeDIC-enabled, fog resource managers. We evaluate MeDIC in four use cases with rigorous simulations, which prove that this collaborative framework not only reduces the uplink traffic but also improves the response time, which is critical in real-time medical applications.

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Towards Medical Data Interoperability Through Collaboration of Healthcare Devices

In a mobile health management system, mobile devices act as the application hosting devices for personal health records (PHRs) and the healthcare servers construct to exchange and analyze PHRs. One of the most popular PHR standards is continuity of care record (CCR). The CCR is expressed in XML formats. However, parsing is an expensive operation that can degrade XML processing performance. Hence, the objective of this study was to identify different operational and performance characteristics for those CCR parsing models including the XML DOM parser, the SAX parser, the PULL parser, and the JSON parser with regard to JSON data converted from XML-based CCR. Thus, developers can make sensible choices for their target PHR applications to parse CCRs when using mobile devices or servers with different system resources. Furthermore, the simulation experiments of four case studies are conducted to compare the parsing performance on Android mobile devices and the server with large quantities of CCR data.

Performance Evaluation of Continuity of Care Records (CCRs): Parsing Models in a Mobile Health Management System

Although smartphone applications represent the most typical data consumer tool from the citizen perspective in environmental applications, they can also be used for in-situ data collection and production in varied scenarios, such as geological sciences and biodiversity. The use of standard protocols, such as SWE, to exchange information between smartphones and sensor infrastructures brings benefits such as interoperability and scalability, but their reliance on XML is a potential problem when large volumes of data are transferred, due to limited bandwidth and processing capabilities on mobile phones. In this article we present a performance analysis about the use of SWE standards in smartphone applications to consume and produce environmental sensor data, analysing to what extent the performance problems related to XML can be alleviated by using alternative uncompressed and compressed formats.

Using SWE Standards for Ubiquitous Environmental Sensing: A Performance Analysis

Parsing is an expensive operation that can degrade XML processing performance. A survey of four representative XML parsing models-DOM, SAX, StAX, and VTD-reveals their suitability for different types of applications.

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XML Document Parsing: Operational and Performance Characteristics

Information identifying a user as having accessed a first network device from an external network connected device is received at an intermediate network connected device from an edge network device. The intermediate network connected device is arranged between the first network device and a second network device. The intermediate network connected device, the first network device and the second network device are within the network and the external network connected device is outside the network. A request to access the second network device is received at the intermediate network connected device from the first network device. It is determined that the user is a source of the request via the first network device based upon the received information. The request from the first network device is evaluated based upon determining the user is the source of the request.

Tak Cheung Lam

Papers

XML Document Parsing: Operational and Performance Characteristics

Role-Based Access to Shared Resources