Earticle

병원 간 전자의무기록 (EHR) 시스템은 중앙 집중식 학습 최적화를 제한하는 개인정보 보호 및 업무량 다양성 문제에 직면해 있다. 본 논문에서는 기관의 자율성을 유지하면서 비용 예측 및 쿼리 실행 효율성을 향상시키는 연합 쿼리 최적화 프레임워크를 구축하고자 한다. 계획 그래프 표현, GNN 기반 비용 학습, 그리고 병원 간 연합 집계 기능을 결합한 연합 그래프 기반 쿼리 최적화 모델인 FedGQO를 제안한다. FedGQO는 원시 EHR 데이터나 상세한 로컬 쿼리 로그를 공유하지 않고도 협업 학습을 가능하게 하며, 비독립 동일분포(non-IID) 워크로드 하에서도 견고함을 유지한다. 실험 결과, FedGQO는 MSE 8.98, 평균 q-오차 1.21, 평균 실행 시간 8.40을 달성하여, 중앙 집중식 상한선에 근접하면서도 기본 최적화기 및 Local-GNN을 능가하는 성능을 보였다. 이러한 결과는 FedGQO가 병원 간 전자의무기록 쿼리 최적화를 위한 효과적이고 개인정보를 보호하는 솔루션임을 입증한다.

Cross-hospital EHR systems face privacy and workload diversity challenges that limit centralized learned optimization. We aim to build a federated query optimization framework that improves cost prediction and query execution efficiency while preserving institutional autonomy. We propose FedGQO, a federated graph-based query optimization model that combines plan-graph representation, GNN-based cost learning, and federated aggregation across hospitals. FedGQO enables collaborative training without sharing raw EHR data or detailed local query logs, while remaining robust under non-IID workloads. In experiments, it achieved an MSE of 8.98, a mean q-error of 1.21, and an average runtime of 8.40, outperforming the native optimizer and Local-GNN while remaining close to the centralized upper bound. These results show that FedGQO is an effective and privacy-preserving solution for cross-hospital EHR query optimization.

Machine learning-based sentiment classification is the best-performing method to understand public sentiment. However, the method has some problems, such as noisy features and high-dimensional feature space which affect the sentiment classification performance. To address the problems, this paper proposes a new feature selection using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights. The proposed method was validated using five tweet datasets on different topics both in Indonesian and English, and compared with state-of-the-art of filter and wrapper-based feature selection methods. Experimental results show the proposed method significantly improves sentiment classification performance and decrease computational time.

기존 SQL 쿼리 최적화 프로그램은 규칙 기반 또는 비용 추정 모델에 크게 의존하는데, 이는 복잡한 다중 조인 또는 중첩 쿼리 시나리오에서 종종 최적이 아닌 실행 계획을 생성한다. 이러한 한계를 해결하기 위해, 실행 계획을 그래프 구조로 변환하고 GCN과 GAT를 활용하여 연산자 종속성과 의미 관계를 모델링하 는 그래프 신경망 기반 쿼리 최적화 프로그램인 GQO를 제안한다. GQO는 정확한 실행 시간 예측을 위한 쿼리 임베딩을 생성하고, PostgreSQL에 삽입하여 실제 성능 향상을 위해 사용할 수 있는 최적화 힌트를 생 성한다. TPC-H 쿼리에 대한 실험 결과는 GQO가 기존 비용 모델, MLP 베이스라인, 그리고 Tree-LSTM 모 델보다 훨씬 뛰어난 성능을 보이며 평균 26% 이상의 성능이 향상되었다.

Traditional SQL query optimizers rely heavily on rule-based or cost-estimation models, which often produce suboptimal execution plans under complex multi-join or nested query scenarios. To address these limitations, we propose GQO, a Graph Neural Network-based Query Optimizer that transforms execution plans into graph structures and leverages both GCN and GAT to model operator dependencies and semantic relationships. GQO generates query embeddings for accurate execution time prediction and produces optimization hints that can be injected into PostgreSQL for real-world performance gains. Experimental results on TPC-H queries demonstrate that GQO significantly outperforms traditional Cost Models, MLP baselines, and Tree-LSTM models, achieving over 26% average performance improvement.

In this paper, recognizing the importance of the database query optimization design methods, we implemented mobile database with mobile program (J2ME) which is a useful database procedures. In doing so, we emphasize the logical query optimization which brings mobile database to performance improvement. The research implies that the suggested mobile program (J2ME) would contribute to the realization of the efficient mobile database as the related technology develops in the future.

The development of the Web has made it easy to access large amounts of data. However, it is one of the big issues how to effectively store and improve query performance about these huge data. This paper proposes an RDF query optimization with hybrid storage structures to solve the issue. Hot-cold detection relocates data properly to improve the RDF query performance. After collecting data access information for a period of time, we identify the data with hot-cold detection. It reduces the write performance degradation of the SSD by moving all hot data at a time to the SSD.

In the modern era, digital data is considered as the more valuable asset of an organization, and the organizations assign more significance to it than the software and hardware assets. Database systems are computer-based record keeping systems, which have been developed to store data for efficient retrieval and processing. One particular approach is the relational databases in which all the information is stored in rows and columns in a series of interconnected tables, and a snippet of structured code called query is used to interact with these database tables. Database management involves indexing of data by tagging information based on some common factors and corresponding criteria. Performance of SQL query against a production database eventually becomes an issue sooner or later. The time-intensive queries not only degrade performance of servers and applications by consuming substantial system resources, but can also result in table locking and data corruption. Therefore, query optimization becomes necessary to prevent performance degradation. Query optimization primarily means selection, followed by sequencing in specific order, of the different SQL clauses to formulate an efficient query from the multiple query plans by drawing a comparison of the query plans based on the cost of the resources involved and the response time. The objective of query optimization is to provide minimum response time and maximum throughput (i.e., the efficient use of resources). This paper reviews different query optimization techniques and approaches discussed in the contemporary literature for both centralized and distributed databases. The paper also highlights merits of these techniques by critically analyzing them.

Query optimization is a very complex task for commercial databases in case of performance issue that needs to be well known of entire structure of database. In desktop or web application at the back end, query processing aspires to be major factor for finding the better execution. We address the problem of SQL query optimization merely from the perspective of query response time in different databases invoked multiple queries are imparted in admission database management system, leveraging join and complex queries. Our proposed method adhering with respect to the underlying topics, to tune (Select, complex and join SQL) queries with optimized execution plan using PL/SQL features by incorporating database objects such as procedures, triggers and methods to improve query performance, instead amendment of query semantics which lessen time of developer or administrator to do manual tuning.

Data analyzing and processing are important tasks in cloud computing. In this field, the MapReduce framework has become a more and more popular tool to analyze large-scale data over large clusters. Compared with the parallel relational database, it has the advantages of excellent scalability and good fault tolerance. However, the performance of join operation using MapReduce is not as good as that of parallel relational database. Thus, how to optimize theta-join operations using MapReduce is an attractive point to which researchers have been paying attention. In this paper, a randomized algorithm named Strict-Even-Join(SEJ) is designed to solve the multi-way theta-joins in a single MapReduce job. Moreover, a dynamic programming algorithm is elaborated to optimize the multi-way theta-joins by calling the SEJ algorithm. The results of experiments show that our approach is feasible and effective.

Multi Query Optimization is one of the most important tasks in Relational Database Management System (RBMS) and it becomes common due to high usage of online decision support management systems in every industry nowadays. In multi query optimization, queries are optimized and executed in batches. However, there are many algorithms use to detect and unified common sub-expressions among multiple queries and unified them so that the more encompassing sub- expression is executed and the other sub-expressions are derived from. In this work, multi-query optimization algorithm using heuristics and semantic approaches was proposed and encoded on SQL Server version 10.0.1600 and three queries were used for the experiment between the proposed algorithm and most recent basic Multi Query Optimization Algorithm (Volcano RU). The result of experiment showed that, Proposed Algorithm gave the best plans compared Volcano RU Algorithm, across all three queries and was best for all queries in terms of execution time and CPU time.

Without multi query optimization, Relational Database Management System for online and analytical decision support systems would have been inefficient and hence unpractical. It is an expensive process because it relies at a great extent on evaluating the different plans (access paths) and choosing an optimal one among them. In Multi Query Optimization, queries are executed in batches and there were many different algorithms acted in such way that, in case some queries have a common sub-expression such a sub- expression is executed once and the output shared. We studied the basic multi query optimization algorithms including Basic Volcano, Volcano-SH and Volcano RU, identified their strengths and weaknesses and recommend strategies for developing new improved multi query optimization algorithm so as to reduce weaknesses and integrate strengths of the different basic multi query algorithms into one efficient algorithm.

Microblog platform, such as Twitter and Sina, has been one of the major ways of information diffusion in modern society. However, although microblog has been proven to contain lots of information, it is really hard for people to find useful information on it. Besides, some information in microblog such as user IDs is not allowed to publish due to privacy policies. Thus the queries on microblog data can be regarded as privacy-preserved queries. One of the challenged issues is the poor performance of answering privacy-preserved queries over microblog data, which owes to the large and increasing volume and the complex social network structure of microblog data. In this paper, we propose a basic idea to optimize the privacy-preserved queries on microblog data. We use a query-specific approach to treat the queries, i.e., the microblog data is first preprocessed according to the specific requirements of different types of queries, which are then organized through some indexing structures. Our preliminary experiments on real microblog data show that this approach has reasonable performance.

Big data analytical systems, such as MapReduce, have become main issues for many enterprises and research groups. Currently, multi-query which translated into MapReduce jobs is submitted repeatedly with similar tasks. So, exploiting these similar tasks can offer possibilities to avoid repeated computations of MapReduce jobs. Therefore, many researches have addressed the sharing opportunity to optimize multi-query processing. Consequently, the main goal of this work is to study and compare comprehensively two existed sharing opportunity techniques using predicate-based filters; MRShare and relaxed MRShare. The comparative study has been performed over TPC-H benchmark and confirmed that the relaxed MRShare technique significantly outperforms the MRShare for shared data in terms of predicate-based filters among multi-query.

In recent years, with the development of the information industry, we ushered in the age of big data, more and more Internet and mobile products are popping up. According to the mobile device is portable, real-time etc, the development of mobile phone system become the focus of scientific and technological development, but the research on its local database is relatively small. This paper introduced a query optimization method based on improved Particle Swarm Optimization algorithm (PSO) for SQLite database on Android platform. This method improves the original PSO, and put the database transaction into the Particle Swarm Optimization algorithm, and should be used to join query. It improves the speed of complex query, and optimizes the query on SQLite database. Experimental results show that this method is an effective way to optimize the SQLite database query, and also can be used in the Android platform.

Multi Query Optimization is an important process in database and it becomes the commonplace due to the frequent usage of decision support systems in almost all the multinational enterprises. The multiple queries from different users that have been addressed to one schema often have a lot of common sub-expressions and it is the function of the multi query optimization algorithms such as Basic Volcano, Volcano RU and Volcano SH algorithms to optimize such multiple queries together and executes the common operation once and share the output among the queries. In this work, a multi query shareability algorithm which can efficiently detect the common sub- expressions among the multiple queries and share the output among those queries was proposed and algorithm for optimal order of those queries was also proposed. The Algorithm has a time complexity of O(n2 + 9n +6) while the most recent basic algorithm thus Volcano RU Algorithm has O(2n2 +20n +12), both the algorithms have O(n2) time complexity which is quadratic in nature. However, the Proposed Algorithm is more efficient and better than Volcano RU algorithm even if n approach to infinity.

In Object-Oriented Databases (OODB), nested object queries are used regularly. Nested structures are put in conditional expressions of the queries in two forms: nested sub-queries or path expression containing hidden joins – nested predicates in WHERE clauses. For nested queries, when analyzing the estimated cost of the nested algebraic expression, the expression evaluation result gives out an ineffective cost. Therefore, our method proposed in this paper will resolve the problems by leveling nested sub-queries in the nested queries. This method will increase the effectiveness of the query processing cost – We use object connection hypergraphs to present nested queries.

The rapid growth in the size of databases and the advances made in Query Languages has resulted in increased SQL query complexity submitted by users, which in turn slows down the speed of information retrieval from the database. The future of high performance database systems lies in parallelism. Commercial vendors’ database systems have introduced solutions but these have proved to be extremely expensive. This paper invistagete how networked resources such as workstations can be utilised by using Parallel Virtual Machine (PVM) to Optimise Database Query Execution. An investigation and experiments of the scalability of the PVM are conducted. PVM is used to implement parallelism in two separate ways: (i) Remove the work load for deriving and maintaining rules from the data server for Semantic Query Optimisation, therefore clears the way for more widespread use of SQO in databases [1,2]. (ii) Answer users queries by a proposed Parallel Query Algorithm PQA which works over a network of workstations, coupled with a sequential Database Management System DBMS called PostgreSql on the prototype called Expandable Server Architecture ESA [1,2,3,4]. Experiments have been conducted to tackle the problems of Parallel and Distributed systems such as task scheduling, load balance and fault tolerance.

Spatial Partitioning Fragmentation (SPF) is a popular method to partition data in Distributed Spatial Databases (DSDBs). The issue of cross-border queries is an inherent problem however with distributed spatial data queries based on partitioning fragmentation given a continuity and strong correlation of geospatial data. In the case of partitioning fragmentation, a global spatial join can be translated into multiple sub-joins, and then divided into 2 groups: Cross-Border Joins (CBJs) and Non-Cross-Border Joins (NCBJs). The CBJ approach is essential for process efficiency in a distributed spatial query. A compound join based on a topological relationship inquiry and a buffering analysis is a crucial class of spatial queries. This article studies compound join optimization for spatial queries in a DSDB, and proposes a set of theorems and rules for the optimization of CBJs, contributing a removal rule and a filtering rule. This article supplies a Partition Fragmentation Join Strategy (PFJS) to resolve the compound join problem based on these rules. Experimental results show that the PFJS can improve the efficiency of CBJs, when compared with the Naive Join Strategy (NJS) or the Spatial Semi-Join Strategy (SSJS). The PFJS contributes to the optimization of spatial compound joins.

The transformation time among queries in the database management system (DBMS) is responsible for the execution time of users' queries, because a conventional DBMS does not consider the transformation cost when queries are transformed for execution. To reduce the transformation time (cost reduction) during execution, we propose an optimal query transformation method by exploring queries from a cost-based point of view. This cost-based point of view means considering the cost whenever queries are transformed for execution. Toward that end, we explore and compare set off heuristic, linear, and exhaustive cost-based transformations. Further, we describe practical methods of cost-based transformation integration and some query transformation problems. Our results show that, some cost-based transformations significantly improve query execution time. For instance, linear and heuristic transformed queries work 43% and 74% better than exhaustive queries.