Summary Due to recent developments in location‐based services and mobile computing, the need for indices for moving objects has been strengthened to improve the response time of a query operation. With a single index in place for managing both the update and query operations for moving objects, the index needs to be updated each time the object moves. This deteriorates the performance of concurrent query operations. It is critical to handle the conflicts between continuous update and query operations effectively using appropriate concurrency control protocol otherwise, inconsistent results will be reported. Many indices have been proposed in the literature for moving objects but they lack the support for processing concurrent operations. Further, the consistent indices in the literature are based on tree structure that have computationally expensive split/merge operations that can negatively affect the response time of query processing algorithms. Moreover such tree based indices are proposed for historical and future timeline data. As the scope of this article is on current timeline and concurrent continuous query operations, therefore, we exploit state of the art hash‐based indices in the literature and presented the two consistent versions. The comparative analysis of the indices is performed and meaningful findings are also presented along.

Summary Sequence alignment is a critical computational problem in various domains, including genomics, proteomics, and natural language processing. The Needleman‐Wunsch (NW) algorithm is a classical dynamic programming approach for finding the optimal global alignment between two sequences. However, its quadratic time and space complexity make it impractical for aligning large‐scale sequences, which are increasingly common in modern applications. In this article, we propose a parallel variation of the NW algorithm that enables scalable global sequence alignment with customizable scoring schemes. Our approach re‐formulates the dependencies in the NW algorithm to enable parallel execution, thereby leveraging the computational power of modern parallel architectures, such as graphics processing unit (GPU). Furthermore, our algorithm supports arbitrary linear scoring schemes, which allows us to use domain‐specific knowledge to improve alignment accuracy. We establish the correctness of our algorithm and evaluate its performance using real DNA and user trajectory sequences on GPUs. Our parallel algorithm has shown impressive results in our experiments, with a peak performance of 27.99 GCUPS (giga cell updates per second) and a maximum speedup of 48.18 times compared to the traditional sequential implementation. Additionally, our algorithm demonstrates remarkable scalability, enabling the alignment of sequences of any length while ensuring balanced work distribution and optimal utilization of resources. Our primary objective is to harness the computational capabilities of a single GPU and fully utilize the processing power of multi‐core CPUs.

Summary Recently, mobile computing has changed the way that spatial data and GIS are processed. Unlike wired and stand‐alone GIS, now the trend has been switched from offline to real‐time data processing using location aware services, such as GPS technology. The increased usage of location aware services in multiuser real‐time environment has made transaction management incredibly significant. If the simultaneous query operations on the same data item are not handled intelligently then this results in data inconsistency issue. Concurrency control protocol is one of the primary aspects that helps in overcoming this issue a in multiuser environment. To the best of our knowledge, the impact of technological advancements on concurrency control has not been thoroughly studied in the literature. In this article, we explored the literature on concurrency control algorithms in depth with respect to real‐time applications and the applications with moving objects. We defined a taxonomy of concurrency control solutions and assessed the maturity of these solutions in the light of characteristics of real‐time and mobile environment. We compared the most recent developments made in the literature and presented meaningful insights. Challenges are also identified and discussed, which can assist in doing research in this domain in future.

Abstract Cyber physical system (CPS) applications are widely used to control critical infrastructure of various application domains, eg, medical health care, energy, and power, to name a few. Such applications usually take input data from sensors, estimate current state of the system, and then based on the estimation, make critical decisions to control the underlying infrastructure automatically. Therefore, security and integrity of the (system state) data are critically important to ensure safe operations of CPS. In this paper, we present a review of security of various data management systems used in CPS. Since CPS are composed of systems of (sub)systems that generate a huge amount of data (ie, periodical sensor input data), therefore, recently, NoSQL and NewSQL data management systems have emerged as popular data management systems to support efficient and scalable analysis of unstructured data. Unfortunately, these systems were not initially build for data security and thus are vulnerable to numerous security attacks. Considering flexible data model and efficient access methods in NoSQL and NewSQL, we discuss the security attacks on such data management systems and their corresponding solutions to mitigate them. In particular, we analyze the system and data security of popular NoSQL and NewSQL systems. To analyze that, we defined feature vectors for system and data security and compared the data systems against them. Finally, we propose security solutions for data management systems by identifying various security vulnerabilities in internal security algorithms of such systems.

Application execution time prediction is of key importance in making decisions about efficient usage of Grid resources. Grid services lack support of a generic application execution time prediction service due to environment specific solutions provided by the existing prediction techniques. To remedy this, we present a generic and comprehensive system to provide execution time predictions of applications on different Grid-sites. Our system is based on a two layered training phase to minimize the training effort, which is our first main contribution. The training phase is driven by a novel experimental design. We also introduce a mechanism of sharing performance measurements across the Grid, on the basis of soft benchmarks, which is our second contribution. Both of these phases support our prediction engine to serve robust predictions. Experiments from the prototype implementation are shown to demonstrate the effectiveness of our proposed system.