Data quality is an important aspect in data application and management, and currency is one of the major dimensions influencing its quality. In real applications, datasets timestamps are often incomplete and unavailable, or even absent. With the increasing requirements to update real-time data, existing methods can fail to adequately determine the currency of entities. In consideration of the velocity of big data, we propose a series of efficient algorithms for determining the currency of dyn...

In Delay Tolerant Networks (DTNs), some routing algorithms ignore that most nodes are selfish, i.e., nodes are willing to use their own resources to forward messages to nodes with whom they have a relationship. In view of this phenomenon, we propose a routing algorithm based on Geographic Information and Node Selfishness (GINS). To choose a forwarding node, GINS combines nodes’ willingness to forward and their geographic information to maximize the possibility of contacting the destination. G...

An ontology is a conceptualisation of domain knowledge. It is employed in semantic web services technologies to describe the meanings of services so that they can be dynamically searched for and composed according to their meanings. It is essential for dynamic service discovery, composition, and invocation. Whether an ontology is well constructed has a tremendous impact on the accuracy of the semantic description of a web service, the complexity of the semantic definitions, the efficiency of ...

This study, based on the theory of equivalence relations, proposes a novel multilevel index model for decentralized service repositories to eliminate redundant information and enhance the time-management quality of the service retrieval process of the service repository architecture. An efficient resource discovery algorithm based on Discrete Hash Tables is presented to enable efficient and effective retrieval services among different distributed repositories. The performance of the proposed ...

It is becoming increasingly difficult for Chinese citizens to access traditional public transport because of overcrowded community structures. Therefore, novel ideas are required to improve the transport system. In this respect, this study considers the design of a public transport scheduling model for a micro system. The model aims to minimize passenger waiting time and maximize number of passengers one bus carries, by simultaneously optimizing departure intervals and use of traditional and ...

Due to the exploding growth in the number of web services, mashup has emerged as a service composition technique to reuse existing services and create new applications with the least amount of effort. Service recommendation is essential to facilitate mashup developers locating desired component services among a large collection of candidates. However, the majority of existing methods utilize service profiles for content matching, not mashup descriptions. This makes them suffer from vocabulary...

Very Long Instruction Word (VLIW) architectures are commonly used in application-specific domains due to their parallelism and low-power characteristics. Recently, parameterization of such architectures allows for runtime adaptation of the issue-width to match the inherent Instruction Level Parallelism (ILP) of an application. One implementation of such an approach is that the event of the issue-width switching dynamically triggers the reconfiguration of the data cache at runtime. In this pap...

One-bit feedback systems generate binary data as their output and the system performance is usually measured by the success rate with a fixed parameter combination. Traditional methods need many executions for parameter optimization. Hence, it is impractical to utilize these methods in Expensive One-Bit Feedback Systems (EOBFSs), where a single system execution is costly in terms of time or money. In this paper, we propose a novel algorithm, named Iterative Regression and Optimization (IRO), ...

We consider a wide range of non-convex regularized minimization problems, where the non-convex regularization term is composite with a linear function engaged in sparse learning. Recent theoretical investigations have demonstrated their superiority over their convex counterparts. The computational challenge lies in the fact that the proximal mapping associated with non-convex regularization is not easily obtained due to the imposed linear composition. Fortunately, the problem structure allows...