Concurrency control for real‐time and mobile transactions: Historical view, challenges, and evolution of practices

Abstract

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. K E Y W O R D S concurrency control, mobile, real time 1 INTRODUCTION IoT (Internet of Things) emerged as a promising technology that has revolutionized the way of living. GPS-based devices and sensors are commonly used in majority of the applications. The convergence of multiple domains such as real-time GIS, mobile computing, and IoT has made it necessary to solve the computing problem considering the requirements of each domain. Mobile GIS domain integrates several technologies: GPS, mobile computing, and GIS. In recent times, the data processing environment deals with varying kinds of sensors. The real-time data collected by sensors need to be processed in timely manner to ensure efficient data management. The sensors can be categorized into two types depending on their mobility: fixed and agile/mobile sensors. Fixed sensors are spatially fixed, whereas mobile sensors change their location with respect to time. Mobile sensors collect data on geographical grounds and this data can be referred to as spatiotemporal data. This has given rise to a new research domain of real-time mobile data processing and management. Thus, real-time processing domain emerged with respect to time depending on the nature of sensor technology and evolution of IoT. The future processing systems are expected to meet the requirements of spatiotemporal and real-time data, leading toward a new domain of real-time GIS. Real-time data can be used for both static and dynamic processing. For example, in the first aspect, mobile sensors can process and analyze the real-time spatiotemporal data for dynamic processing. On the other hand, real-time data can be collected and saved in a log for future offline analysis (static processing).

Exploiting hashing for concurrent query processing and indexing of current location of moving objects

“…As discussed before, the tree-based indices have limitations such as unbalanced load, an overhead of locking the whole sub branch of tree, an overhead of excessive locking due to node overlap issue and dead space issue, an overhead of computationally expensive split/merge operations due to frequent location updates of moving objects. 26 In contrast to…”

“…This is because the concurrent operations accessing the data simultaneously for read-only purpose will not make any changes to the data and thus the access will F I G U R E 6 Linear hashing. 26 be safe. However, in exclusive mode, once an exclusive lock is acquired by any operation against a shared resource, then it cannot be accessed by any other operation.…”

“…Each category of concurrency control protocol has some pros and cons at broader level. The pros and cons have been highlighted and discussed in Reference 26. Lock‐based solutions provide best consistency and serializability but are not preferred for query intensive applications.…”

“…Likewise, there are many concurrency control protocols proposed in the literature for mobile environment. A detailed analysis and comparison of such protocols has been made in Reference 26. However, our focus is on designing of consistent index for moving objects not just a concurrency control algorithm.…”

“…But, tree‐based indices have limitations such as unbalanced load, an overhead of locking the whole sub branch of tree, an overhead of excessive locking due to node overlap issue and dead space issue, an overhead of computationally expensive split/merge operations due to frequent location updates of moving objects. These challenges are discussed and analyzed in detail in Reference 26. In contrast to it, hash‐based solutions result into balanced load that result in uniform distribution of records.…”