A Survey on String Constraint Solving

String constraint solving refers to solving combinatorial problems involving constraints over string variables. String solving approaches have become popular over the past few years given the massive use of strings in different application domains like formal analysis, automated testing, database query processing, and cybersecurity. This article reports a comprehensive survey on string constraint solving by exploring the large number of approaches that have been proposed over the past few decades to solve string constraints.

Anytime Ranking on Document-Ordered Indexes

Inverted indexes continue to be a mainstay of text search engines, allowing efficient querying of large document collections. While there are a number of possible organizations, document-ordered indexes are the most common, since they are amenable to various query types, support index updates, and allow for efficient dynamic pruning operations. One disadvantage with document-ordered indexes is that high-scoring documents can be distributed across the document identifier space, meaning that index traversal algorithms that terminate early might put search effectiveness at risk. The alternative is impact-ordered indexes, which primarily support top- disjunctions but also allow for anytime query processing, where the search can be terminated at any time, with search quality improving as processing latency increases. Anytime query processing can be used to effectively reduce high-percentile tail latency that is essential for operational scenarios in which a service level agreement (SLA) imposes response time requirements. In this work, we show how document-ordered indexes can be organized such that they can be queried in an anytime fashion, enabling strict latency control with effective early termination. Our experiments show that processing document-ordered topical segments selected by a simple score estimator outperforms existing anytime algorithms, and allows query runtimes to be accurately limited to comply with SLA requirements.

Developing an Efficient Secure Query Processing Algorithm on Encrypted Databases using Data Compression

Distributed computing includes putting aside the data utilizing outsider storage and being able to get to this information from a place at any time. Due to the advancement of distributed computing and databases, high critical data are put in databases. However, the information is saved in outsourced services like Database as a Service (DaaS), security issues are raised from both server and client-side. Also, query processing on the database by different clients through the time-consuming methods and shared resources environment may cause inefficient data processing and retrieval. Secure and efficient data regaining can be obtained with the help of an efficient data processing algorithm among different clients. This method proposes a well-organized through an Efficient Secure Query Processing Algorithm (ESQPA) for query processing efficiently by utilizing the concepts of data compression before sending the encrypted results from the server to clients. We have addressed security issues through securing the data at the server-side by an encrypted database using CryptDB. Encryption techniques have recently been proposed to present clients with confidentiality in terms of cloud storage. This method allows the queries to be processed using encrypted data without decryption. To analyze the performance of ESQPA, it is compared with the current query processing algorithm in CryptDB. Results have proven the efficiency of storage space is less and it saves up to 63% of its space.

Query Processing of Geosocial Data in Location-Based Social Networks

The increasing use of social media and the recent advances in geo-positioning technologies have produced a great amount of geosocial data, consisting of spatial, textual, and social information, to be managed and queried. In this paper, we focus on the issue of query processing by providing a systematic literature review of geosocial data representations, query processing methods, and evaluation approaches published over the last two decades (2000–2020). The result of our analysis shows the categories of geosocial queries proposed by the surveyed studies, the query primitives and the kind of access method used to retrieve the result of the queries, the common evaluation metrics and datasets used to evaluate the performance of the query processing methods, and the main open challenges that should be faced in the near future. Due to the ongoing interest in this research topic, the results of this survey are valuable to many researchers and practitioners by gaining an in-depth understanding of the geosocial querying process and its applications and possible future perspectives.

An Efficient Distributed SPARQL Query Processing Scheme Considering Communication Costs in Spark Environments

Various distributed processing schemes were studied to efficiently utilize a large scale of RDF graph in semantic web services. This paper proposes a new distributed SPARQL query processing scheme considering communication costs in Spark environments to reduce I/O costs during SPARQL query processing. We divide a SPARQL query into several subqueries using a WHERE clause to process a query of an RDF graph stored in a distributed environment. The proposed scheme reduces data communication costs by grouping the divided subqueries in related nodes through the index and processing them, and the grouped subqueries calculate the cost of all possible query execution paths to select an efficient query execution path. The efficient query execution path is selected through the algorithm considering the data parsing cost of all possible query execution paths, amount of data communication, and queue time per node. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

Query Processing in Blockchain Systems: Current State and Future Challenges

When, in 2008, Satoshi Nakamoto envisioned the first distributed database management system that relied on cryptographically secured chain of blocks to store data in an immutable and tamper-resistant manner, his primary use case was the introduction of a digital currency. Owing to this use case, the blockchain system was geared towards efficient storage of data, whereas the processing of complex queries, such as provenance analyses of data history, is out of focus. The increasing use of Internet of Things technologies and the resulting digitization in many domains, however, have led to a plethora of novel use cases for a secure digital ledger. For instance, in the healthcare sector, blockchain systems are used for the secure storage and sharing of electronic health records, while the food industry applies such systems to enable a reliable food-chain traceability, e.g., to prove compliance with cold chains. In these application domains, however, querying the current state is not sufficient—comprehensive history queries are required instead. Due to these altered usage modes involving more complex query types, it is questionable whether today’s blockchain systems are prepared for this type of usage and whether such queries can be processed efficiently by them. In our paper, we therefore investigate novel use cases for blockchain systems and elicit their requirements towards a data store in terms of query capabilities. We reflect the state of the art in terms of query support in blockchain systems and assess whether it is capable of meeting the requirements of such more sophisticated use cases. As a result, we identify future research challenges with regard to query processing in blockchain systems.

Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics

With the advent of wireless sensor networks and their deep integration with the world have enabled users worldwide to achieve benefits from location-based services through mobile applications, the problems such as low bandwidth, high network traffic, and disconnections issues are normally extracted from mobile services. An efficient database system is required to manage mentioned problems. Our research work finds the probability of user’s next locations. A mobile user (query issuer) changes its position when performing a specific mobile search, where these queries change and repeat the search with the issuer position. Moreover, the query issuer can be static and may perform searches with varying conditions of queries. Data is exchanged with mobile devices and questions that are formulated during searching for query issuer locations. An aim of the research work is achieved through effectively processing of queries in terms of location-dependent, originated by mobile users. Significant studies have been performed in this field in the last two decades. In this paper, our novel approach comprise of usage of semantic caches with the Bayesian networks using a prediction algorithm. Our approach is unique and distinct from the traditional query processing system especially in mobile domain for the prediction of future locations of users. Consequently, a better search is analyzed using the response time of data fetch from the cache.