Abstract-Distributed nature of transactions arising at different sites and needing resources from diverse locations pose various operational problems, such as deadlocks, concurrency and data recovery. A deadlock may occur when a transaction enters into wait state that request resource from other blocked transactions. Deadlock detection and resolving is very difficult in a distributed database system because no controller has complete and current information about the system and data dependencies. In this paper, an enhanced technique for deadlock resolution is presented, which minimizes the abortion or waiting of the selected victim transactions. The proposed system includes the use of fuzzy logic by creating a set of fuzzy rules in order to deal with criticalness and similarity attributes of transactions. By using these rules, fuzzy logic will try to provide an easy conflict resolution method between transactions to diminish transactions wasted restart, and guaranteeing temporal consistency of data and transactions. Furthermore, the presented deadlock handling algorithm does not detect any false deadlock or exclude any really existing deadlocks. Experimental results show performance of the recommended system benefits such as increase in commit rate and decrease in re-execution or waiting of the transactions.

The adoption of electronic patient records in hospitals will provide numerous benefits, but it will also present new issues. One of these is the creation of deadlocks, which causes delays in conducting tasks such as obtaining patient information. If the underlying system is distributed, the deadlock situation gets even more problematic. The strategy for preventing deadlock requires basic information from the transaction structure and requested resources. However, in most circumstances, this information is either unavailable or ambiguous. Because the cost of processing and storing sophisticated structures (e.g., wait-for graphs) is so high for a system in comparison to an array of structures, most discovery and resolved-deadlock protocols use them. Due to imprecise knowledge in specifying the transactions' attributes, distributed deadlock resolution methods present a new problem in dealing with uncertainty. To address this problem, a variety of fuzzy logic-based techniques have been proposed. However, very little attention has been paid to dealing with ambiguous, vague, incomplete, and inconsistent information about transaction attributes in a single framework. In this paper, we used neutrosophic logic, a generalization of fuzzy logic, to solve the problem of uncertainty in distributed real-time deadlock-resolving systems. The proposed method is structured to reflect multiple types of knowledge and relations among all features and tripartitions the transactions' features include validation factor degree, slackness degree, degree of deadline-missed transaction based on the degree of membership of truthiness, degree of membership of indeterminacy, and degree of membership of falsity. We developed a tool set and conducted experiments using benchmark datasets. There is an increase in detection rate and a large drop in rollback rate when this new strategy is used. Our technology resolved all database deadlocks and significantly increased database performance by up to three orders of magnitude.INDEX TERMS Deadlock recovery, neutrosophic set, healthcare databases, distributed deadlock detection. I. INTRODUCTIONNowadays, we depend heavily on online healthcare systems to ensure that medical treatments are always available. These systems depend largely on an interconnected network for data exchange between diverse entities, including as patients, physicians, and nurses. This results in massive amounts of data being maintained in medical databases, including details of surgeries, drugs, and allergies, among other things. Clinical information systems that are properly developed will allow physicians to obtain pertinent medical information about any patient, anytime they need it. However, one possible issue that users of electronic systems may confront is occasional delays in obtaining information caused by computer freezes. Deadlock is a common cause of a computer seeming to freeze. During the electronic healthcare records (HIMS) pilot phase, and as the number of users increased, the necessity to handle deadlock and concurren...

Electronic patient data gives many advantages, but also new difficulties. Deadlocks may delay procedures like acquiring patient information. Distributed deadlock resolution solutions introduce uncertainty due to inaccurate transaction properties. Soft computing-based solutions have been developed to solve this challenge. In a single framework, ambiguous, vague, incomplete, and inconsistent transaction attribute information has received minimal attention. The work presented in this paper employed type-2 neutrosophic logic, an extension of type-1 neutrosophic logic, to handle uncertainty in real-time deadlock-resolving systems. The proposed method is structured to reflect multiple types of knowledge and relations among transactions' features that include validation factor degree, slackness degree, degree of deadline-missed transaction based on the degree of membership of truthiness, degree of membership of indeterminacy, and degree of membership of falsity. Here, the footprint of uncertainty (FOU) for truth, indeterminacy, and falsity represents the level of uncertainty that exists in the value of a grade of membership. We employed a distributed real-time transaction processing simulator (DRTTPS) to conduct the simulations and conducted experiments using the benchmark Pima Indians diabetes dataset (PIDD). As the results showed, there is an increase in detection rate and a large drop in rollback rate when this new strategy is used. The performance of Type-2 neutrosophicbased resolution is better than the Type-1 neutrosophic-based approach on the execution ratio scale. The improvement rate has reached 10% to 20%, depending on the number of arrived transactions.