Query Optimization Algorithm Research Articles

An optimal secure defense mechanism for DDoS attack in IoT network using feature optimization and intrusion detection system

Now, the Cyber security is facing unprecedented difficulties as a result of the proliferation of smart devices in the Internet of Things (IoT) environment. The rapid growth in the number of Internet users over the past two decades has increased the need for cyber security. Users have provided new opportunities for attackers to do harm. Limited security budgets leave IoT devices vulnerable and easily hacked to launch distributed denial-of-service (DDoS) attacks, with disastrous results. Unfortunately, due to the unique nature of the Internet of Things environment, most security solutions and intrusion detection systems (IDS) cannot be directly adapted to the IoT with acceptable security performance and are vulnerable to various attacks that do not benefit. In this paper we propose an optimal secure defense mechanism for DDoS in IoT network using feature optimization and intrusion detection system (OSD-IDS). In OSD-IDS mechanism, first we introduce an enhanced ResNet architecture for feature extraction which extracts more deep features from given traffic traces. An improved quantum query optimization (IQQO) algorithm for is used feature selection to selects optimal best among multiple features which reduces the data dimensionality issues. The selected features have given to the detection and classification module to classify the traffic traces are affected by intrusion or not. For this, we design a fast and accurate intrusion detection mechanism, named as hybrid deep learning technique which combines convolutional neural network (CNN) and diagonal XG boosting (CNN-DigXG) for the fast and accurate intrusion detection in IoT network. Finally, we validate the performance of proposed technique by using different benchmark datasets are BoNeSi-SlowHTTPtest and CIC-DDoS2019. The simulation results of proposed IDS mechanism are compared with the existing state-of-art IDS mechanism and analyze the performance with respects to different statistical measures. The results show that the DDoS detection accuracy of proposed OSD-IDS mechanism is high as 99.476% and 99.078% for BoNeSi-SlowHTTPtest, CICDDoS2019, respectively.

Query Optimization in Distributed Database Based on Improved Artificial Bee Colony Algorithm

Query optimization is one of the key factors affecting the performance of database systems that aim to enact the query execution plan with minimum cost. Particularly in distributed database systems, due to the multiple copies of the data that are stored in different data nodes, resulting in the dramatic increase in the feasible query execution plans for a query statement. Because of the increasing volume of stored data, the cluster size of distributed databases also increases, resulting in poor performance of current query optimization algorithms. In this case, a dynamic perturbation-based artificial bee colony algorithm is proposed to solve the query optimization problem in distributed database systems. The improved artificial bee colony algorithm improves the global search capability by combining the selection, crossover, and mutation operators of the genetic algorithm to overcome the problem of falling into the local optimal solution easily. At the same time, the dynamic perturbation factor is introduced so that the algorithm parameters can be dynamically varied along with the process of iteration as well as the convergence degree of the whole population to improve the convergence efficiency of the algorithm. Finally, comparative experiments conducted to assess the average execution cost of Top-k query plans generated by the algorithms and the convergence speed of algorithms under the conditions of query statements in six different dimension sets. The results demonstrate that the Top-k query plans generated by the proposed method have a lower execution cost and a faster convergence speed, which can effectively improve the query efficiency. However, this method requires more execution time.

Research on Welding Quality Traceability Model of Offshore Platform Block Construction Process

Quality traceability plays an essential role in assembling and welding offshore platform blocks. The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platform and the process level of the offshore industry. Currently, quality management remains in the era of primary information, and there is a lack of effective tracking and recording of welding quality data. When welding defects are encountered, it is difficult to rapidly and accurately determine the root cause of the problem from various complexities and scattered quality data. In this paper, a composite welding quality traceability model for offshore platform block construction process is proposed, it contains the quality early-warning method based on long short-term memory and quality data backtracking query optimization algorithm. By fulfilling the training of the early-warning model and the implementation of the query optimization algorithm, the quality traceability model has the ability to assist enterprises in realizing the rapid identification and positioning of quality problems. Furthermore, the model and the quality traceability algorithm are checked by cases in actual working conditions. Verification analyses suggest that the proposed early-warning model for welding quality and the algorithm for optimizing backtracking requests are effective and can be applied to the actual construction process.

DLBN: Group Storage Mechanism Based on Double-Layer Blockchain Network

Blockchain, which stores data in an appending form, cannot achieve the purpose of expanding the storage capacity by increasing the number of nodes. As the system runs, nodes will face problems of insufficient storage space. In the existing peer-to-peer (P2P) blockchain network model, all network nodes participate in data storage, and the generated blocks need to be verified among the network-wide nodes. This approach suffers from low system transaction throughput and data storage redundancy. In order to solve the above existing problems, this article proposes a block data storage model based on the double-layer blockchain network (DLBN), which improves the internal data composition structure of the blockchain. The DLBN contains two types of blockchain nodes, which form the storage and consensus layers of the system, respectively. The consensus layer is responsible for tasks, such as transaction sequencing, validation, and block packing, thus increasing the system transaction throughput. The nodes in the storage layer are divided into multiple storage units (SUs), and all nodes in the SU jointly maintain a copy of the complete blockchain, thereby reducing the storage pressure on the nodes. Based on the DLBN model, we design a reputation-based consensus mechanism, block storage allocation algorithm, and transaction query optimization algorithm, respectively. Through experimental verification and analysis, the storage model based on the DLBN can effectively improve the system transaction throughput and reduce the node storage capacity while ensuring system security.

Query Optimization for Distributed Spatio-Temporal Sensing Data Processing

The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data geometry characteristics, complex anomalistic spatial regions, unique query patterns, and so on. Timely and efficient spatio-temporal querying significantly improves the accuracy and intelligence of processing sensing data. Most existing query algorithms show their lack of supporting spatio-temporal queries and irregular spatial areas. In this paper, we propose two spatio-temporal query optimization algorithms based on SpatialHadoop to improve the efficiency of query spatio-temporal sensing data: (1) spatio-temporal polygon range query (STPRQ), which aims to find all records from a polygonal location in a time interval; (2) spatio-temporal k nearest neighbors query (STkNNQ), which directly searches the query point’s k closest neighbors. To optimize the STkNNQ algorithm, we further propose an adaptive iterative range optimization algorithm (AIRO), which can optimize the iterative range of the algorithm according to the query time range and avoid querying irrelevant data partitions. Finally, extensive experiments based on trajectory datasets demonstrate that our proposed query algorithms can significantly improve query performance over baseline algorithms and shorten response time by 81% and 35.6%, respectively.

An Efficient Query Optimizer with Materialized Intermediate Views in Distributed and Cloud Environment

In cloud computing environment hardware resources required for the execution of query using distributed relational database system are scaled up or scaled down according to the query workload performance. Complex queries require large scale of resources in order to complete their execution efficiently. The large scale of resource requirements can be reduced by minimizing query execution time that maximizes resource utilization and decreases payment overhead of customers. Complex queries or batch queries contain some common subexpressions. If these common subexpressions evaluated once and their results are cached, they can be used for execution of further queries. In this research, we have come up with an algorithm for query optimization, which aims at storing intermediate results of the queries and use these by-products for execution of future queries. Extensive experiments have been carried out with the help of simulation model to test the algorithm efficiency

Research on Query Optimization of Classic Art Database Based on Artificial Intelligence and Edge Computing

With the changes and development of the social era, my country’s classic art is slowly being lost. In order to more effectively inherit and preserve classic art, the collection and sorting of classic art data through modern information technology has become a top priority. Database storage is a good way. However, as the amount of data grows, the requirements for computing processing power and query speed for massive amounts of data and information are also increasing day by day. Faced with this problem, this article is aimed at studying the optimization of database queries through effective algorithms to improve the efficiency of data query. Based on the traditional database query optimization algorithm, this article improves on the traditional algorithm and proposes a semi‐join query optimization algorithm, which reduces the number of connection cards and the number of columns and uses the number of blocks that participate in the semi‐link algorithm connection and preconnection preview and selection. And other functions reduce the size of the participating block, and the connection sent between sites reduces the cost of sending between networks. The graph data query optimization algorithm is used to optimize the graph data query in the database to reduce the extra task overhead and improve the system performance. The experimental results of this paper show that through the data query optimization algorithm of this paper, the additional task overhead is reduced by 19%, the system performance is increased by 22%, and the data query efficiency is increased by 31%.

Query Optimization Algorithm of Replication Join Based on Sampling Partition

Aiming at the low efficiency of join query in MapReduce traditional partition join algorithm when data skew, a replication join optimization algorithm based on sampling partition is proposed. According to the sampled statistics of connection attribute data, the algorithm divides the datasets in connection relationship into skewed data subset and non skewed data subset. In order to optimize the query performance, join query processing is carried out on them respectively. For the join queries of non skewed data subsets, the improved consistency hash function is used to partition these subsets, so that the load of data connection query processing of each node is balanced. For the skewed data subset join query, the smaller skewed data subsets are distributed to each node, and the larger skewed data subsets are partitioned according to the non skewed fields. In the Reduce stage, these skewed data subsets are join queried. Experiments show that the algorithm can optimize the join query performance under different data skew rates, and achieve efficient join query processing of large datasets.

Evolutionary Fuzzy‐based gravitational search algorithm for query optimization in crowdsourcing system to minimize cost and latency

AbstractCrowdsourcing is an environment where a group of users collaborates together to exchange information and to find answers for complex problems (queries). Query optimization is the task of selecting the best query strategy with less cost associated with it. The crowdsourcing cost can be determined by selecting the best plan from the set of options available and the best plan considerably reduce the cost for the inquiry configuration. As one of the center tasks in information recovery, the investigation of top‐k queries with crowdsourcing, to be specific group empowered top k inquiries is depicted. This issue is defined with three key variables, latency, money related expense, and nature of answers. The fundamental point is to plan a novel system that limits financial cost when the latency is compelled. In this article, we used a heuristic search algorithm named as Evolutionary Fuzzy‐based Gravitational Search algorithm (EFGSA) that produces an optimal query feature selection results with minimizing cost and latency. EFGSA‐based crowdsourcing framework gives a better balance between latency and cost while generating query plans. The performance analysis of proposed EFSGA for optimal query plan is evaluated in terms of running time, accuracy, monetary cost, and so on. From the experimental results, the proposed method achieved better results than other methods in our cost and latency model.

Research on Group Reverse Farthest Neighbour Query Algorithm

In recent years, the demand for location-based services is growing. From resource tracking to personal life assistance, spatial data query technology plays an important role, especially the application of reverse nearest neighbor query technology in decision support, resource allocation optimization, data mining. By contrast, the reverse farthest neighbor query has become a more and more hot topic in the research of spatial database theory in recent years. The purpose of the reverse farthest neighbor query is to obtain the final result sets with the given point as its farthest neighbor, which is specially used to solve the problem of weak influence set in space. This paper gives a query optimization algorithm based on the least covering circle. The algorithm firstly introduces the minimal covering circle algorithm. Secondly, the p-ray pruning algorithm is used to filter the candidate set in the second level, and the candidate set is modified by the reverse far-neighborhood range query algorithm to obtain the final result set, which solves the problem in Euclidean effectively. Through specific comparative experimental analysis, c-grfn algorithm has good query performance not only in random distributed environment, but also in Gaussian distribution. It usually has 20% advantages of time in distributed space. As the data set increases, the time advantage becomes more obvious

Analysis of Two Phase Query Optimization Algorithm for Generating Optimal Query Plan using Randomized Algorithm

Analysis of Two Phase Query Optimization Algorithm for Generating Optimal Query Plan using Randomized Algorithm

Query Optimization : A Metaheuristics Approach Using Modified Memetics Algorithm (MMA)

The more complex business process of a system, the greater data that is stored. Increase of data transactions has an impact on a system performance. Therefore it is needed to optimize the query processing on data storage to maintain and improve the system performance. A memetics algorithm (MA) is a population-based metaheuristics approach which is the development of traditional genetics algorithms (GA) combined with local search (LS) technique. By using tabu search (TS) technique on the crossover operation in GA, this research proposes the modified memetics algorithm (MMA) for query optimization. The result shows that the processing time of the optimized (MMA) query is faster than the unoptimized query.

A Framework for Query Optimization Algorithms for Biological Data

Recently, the size of biological databases has significantly increased, with a growing number of users and rates of queries. As a result, some databases have reached a terabyte size. On the other hand, the need to access the databases at the fastest possible rates is increasing. At this point, the computer scientists could assist to organize the data and query in a way that allows biologists to quickly search existing information. In this paper, a query model for DNA and protein sequence datasets is proposed. This method of dealing with the query can effectively and rapidly retrieve all similar proteins/DNA from a large database. A theoretical and conceptual proposed framework is derived using query techniques form different applications. The results show that the query optimization algorithms reduce the query processing time in comparison with the normal query searching algorithm.

Evolutionary Algorithms for Query Op-timization in Distributed Database Sys-tems: A review

Evolutionary Algorithms are bio-inspired optimization problem-solving approaches that exploit principles of biological evolution. , such as natural selection and genetic inheritance. This review paper provides the application of evolutionary and swarms intelligence based query optimization strategies in Distributed Database Systems. The query optimization in a distributed environment is challenging task and hard problem. However, Evolutionary approaches are promising for the optimization problems. The problem of query optimization in a distributed database environment is one of the complex problems. There are several techniques which exist and are being used for query optimization in a distributed database. The intention of this research is to focus on how bio-inspired computational algorithms are used in a distributed database environment for query optimization. This paper provides working of bio-inspired computational algorithms in distributed database query optimization which includes genetic algorithms, ant colony algorithm, particle swarm optimization and Memetic Algorithms.

Data skyline query protocol based on parallel genetic improvement decision tree

Query optimization of database requires high speed and high efficiency. In order to solve the low efficiency problem and difficulty in obtaining optimal solution existing in the current query optimization algorithm of database, a query optimization of database based on multi-group firefly algorithm (MGFA) is proposed, combining with characteristics of database query and advantage of firefly algorithm. Firstly, the firefly group is divided into multiple groups with different parameters, and each group of fireflies followed the optimal firefly in the same group for optimizing. Then, mutual learning mechanism is established among various groups of optimal fireflies to realize inter-group information exchange. At last, query optimization data of database are adopted for the simulation experiment. Experiment results indicate that MGFA is a query optimization method of database with good performance. It can obtain better query result than other algorithms do.

A DSP-Topk Query Optimization Algorithm Supporting Indoor Obstacle Space

A DSP-Topk Query Optimization Algorithm Supporting Indoor Obstacle Space

Multi Query Optimization Algorithm Using Semantic and Heuristic Approaches

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.

Parallelizing query optimization on shared-nothing architectures

Data processing systems offer an ever increasing degree of parallelism on the levels of cores, CPUs, and processing nodes. Query optimization must exploit high degrees of parallelism in order not to gradually become the bottleneck of query evaluation. We show how to parallelize query optimization at a massive scale. We present algorithms for parallel query optimization in left-deep and bushy plan spaces. At optimization start, we divide the plan space for a given query into partitions of equal size that are explored in parallel by worker nodes. At the end of optimization, each worker returns the optimal plan in its partition to the master which determines the globally optimal plan from the partition-optimal plans. No synchronization or data exchange is required during the actual optimization phase. The amount of data sent over the network, at the start and at the end of optimization, as well as the complexity of serial steps within our algorithms increase only linearly in the number of workers and in the query size. The time and space complexity of optimization within one partition decreases uniformly in the number of workers. We parallelize single- and multi-objective query optimization over a cluster with 100 nodes in our experiments, using more than 250 concurrent worker threads (Spark executors). Despite high network latency and task assignment overheads, parallelization yields speedups of up to one order of magnitude for large queries whose optimization takes minutes on a single node.

Efficient Algorithm for Multi Query Optimization

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(n 2 + 9n +6) while the most recent basic algorithm thus Volcano RU Algorithm has O(2n 2 +20n +12), both the algorithms have O(n 2 ) 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.

On Multi Query Optimization Algorithms Problem

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.