Multi Query Research Articles

MMViT-Seg: A lightweight transformer and CNN fusion network for COVID-19 segmentation

Background and objective: COVID-19 is a serious threat to human health. Traditional convolutional neural networks (CNNs) can realize medical image segmentation, whilst transformers can be used to perform machine vision tasks, because they have a better ability to capture long-range relationships than CNNs. The combination of CNN and transformers to complete the task of semantic segmentation has attracted intense research. Currently, it is challenging to segment medical images on limited data sets like that on COVID-19.Methods: This study proposes a lightweight transformer+CNN model, in which the encoder sub-network is a two-path design that enables both the global dependence of image features and the low layer spatial details to be effectively captured. Using CNN and MobileViT to jointly extract image features reduces the amount of computation and complexity of the model as well as improves the segmentation performance. So this model is titled Mini-MobileViT-Seg (MMViT-Seg). In addition, a multi query attention (MQA) module is proposed to fuse the multi-scale features from different levels of decoder sub-network, further improving the performance of the model. MQA can simultaneously fuse multi-input, multi-scale low-level feature maps and high-level feature maps as well as conduct end-to-end supervised learning guided by ground truth.Results: The two-class infection labeling experiments were conducted based on three datasets. The final results show that the proposed model has the best performance and the minimum number of parameters among five popular semantic segmentation algorithms. In multi-class infection labeling results, the proposed model also achieved competitive performance.Conclusions: The proposed MMViT-Seg is tested on three COVID-19 segmentation datasets, with results showing that this model has better performance than other models. In addition, the proposed MQA module, which can effectively fuse multi-scale features of different levels further improves the segmentation accuracy.

Multi Object and Dynamic Query Based CBIR System using DCT Incorporated with HOG and HTF

This work contributes multi object detection and dynamic query image based retrieval system. Generally, finding relevance and matching user expectations is very critical based on query key information and these results irrelevant responses which will produce low similarity index. Consequently, CBIR system took a major responsibility of identifying new objects, retrieving similar objects or contents based on multi query and dynamic keywords with improved recall and precision as per requirement of the users. At this juncture, Discrete Curvelet Transform with the incorporation of HOG and HTF based approach is proposed to handle commercial image, medical images and types of multi model images. This proposed approach mainly focuses on extracting scaled features for finding correlation among the query and database images. To start with the process, query image is decomposed into multi level sub images to extract set of texture features at two levels. These features are estimated by Gray Level Co-occurrence Matrix (GLCM) and HOG descriptor based techniques is adapted to find scaled vectors with reduced dimensionality. This method outperform compared as compared to existing method is authenticated from experimental results.

Multi Object and Dynamic Query Based CBIR System using DCT Incorporated with HOG and HTF

This work contributes multi object detection and dynamic query image based retrieval system. Generally, finding relevance and matching user expectations is very critical based on query key information and these results irrelevant responses which will produce low similarity index. Consequently, CBIR system took a major responsibility of identifying new objects, retrieving similar objects or contents based on multi query and dynamic keywords with improved recall and precision as per requirement of the users. At this juncture, Discrete Curvelet Transform with the incorporation of HOG and HTF based approach is proposed to handle commercial image, medical images and types of multi model images. This proposed approach mainly focuses on extracting scaled features for finding correlation among the query and database images. To start with the process, query image is decomposed into multi level sub images to extract set of texture features at two levels. These features are estimated by Gray Level Co-occurrence Matrix (GLCM) and HOG descriptor based techniques is adapted to find scaled vectors with reduced dimensionality. This method outperform compared as compared to existing method is authenticated from experimental results.

An Active Genomic Data Recovery Attack

With the decreasing cost and availability of human genome sequencing, genomic privacy becomes an important issue. Several methods have been proposed in the literature to overcome these problems including cryptographic and privacy preserving data mining methods: homomorphic encryption, cryptographic hardware. In a recent work, Barman et. al studied privacy threats and practical solutions considering an SNP based scenario. The authors introduced a new protocol where a malicious medical center processes an active attack in order to retrieve genomic data of a given patient. The authors have mentioned that this protocol provides a trade-off between privacy and practicality. In this paper, we first give an overview of the system for SNP based risk calculation. We provide the definitions of privacy threats and briefly Barman et al.’s protocol and their solution. The authors proposed to use a weighted sum method of SNP coefficients for calculating disease tendency. They argue that the specific choice of the bases would prevent unique identification of SNPs. Our main observation is that this is not true. Contrary to the security claim, SNP combinations can be identified uniquely in many different scenarios. Our method exploits a pre-computed look-up table for retrieving SNPs’ values from the test result. An attacker can obtain all SNP values of a given patient by using the pre-computed look-up table. We provide practical examples of weights and pre-computed tables. We also mention that even in the case where the table is large and the attacker can not handle at one time, he can still gather information using multi queries. Our work shows that more realistic attack scenarios must be considered in the design of genetic security systems.

Multiobjective Optimal Control Methods for the Navier-Stokes Equations Using Reduced Order Modeling

In a wide range of applications it is desirable to optimally control a dynamical system with respect to concurrent, potentially competing goals. This gives rise to a multiobjective optimal control problem where, instead of computing a single optimal solution, the set of optimal compromises, the so-called Pareto set, has to be approximated. When the problem under consideration is described by a partial differential equation (PDE), as is the case for fluid flow, the computational cost rapidly increases and makes its direct treatment infeasible. Reduced order modeling is a very popular method to reduce the computational cost, in particular in a multi query context such as uncertainty quantification, parameter estimation or optimization. In this article, we show how to combine reduced order modeling and multiobjective optimal control techniques in order to efficiently solve multiobjective optimal control problems constrained by PDEs. We consider a global, derivative free optimization method as well as a local, gradient based approach for which the optimality system is derived in two different ways. The methods are compared with regard to the solution quality as well as the computational effort and they are illustrated using the example of the two-dimensional incompressible flow around a cylinder.

Ontology Driven Cross-Linked Domain Data Integration and Spatial Semantic Multi Criteria Query System for Geospatial Public Health

This article describes how public health information management is an interdisciplinary application which deals with cross linked application domains. Geospatial environment, place and meteorology parameters effect public health. Effective decision making plays a vital role and requires disease data analysis which in turn requires effective Public Health Knowledge Base (PHKB) and a strong efficient query engine. Ontologies enhance the performance of the retrieval system and achieve application interoperability. The current research aims at building PHKB through ontology based cross linked domain integration. It designs a dynamic GeoSPARQL query building from simple form based query composition. The spatial semantic multi criteria query engine is developed by identifying all possible query patterns considering the ontology elements and multi criteria from cross linked application domains. The research has adopted OGC, W3C, WHO and mHealth standards.

Multi connection query optimization in data warehouse dependent on multiple linear regression algorithm

ABSTRACTThe traditional query optimization method is to reduce the size of the intermediate relationship as much as possible when calculating the multi relation connection. It does not take into account the mass of data in the data warehouse, which is read mainly and the fact table is generally indexed often cannot obtain the disadvantage of the optimal effect, and puts forward the data warehouse suitable for frequent additions and deletions query environment. Multi connection query optimization in data warehouse, MCQODW) method: By using multiple linear regression algorithm to write the detection sequence of data, and then to optimize the data warehouse multi connection query without changing the sequence of other query detection sequences, in order to reduce repeated calculation. Theoretical analysis and experimental results show that the algorithm reduces significantly in the data warehouse multi join query optimization and execution time, and the method is effective.

ENHANCED MULTIQUERY SYSTEM USING KNN FOR CONTENT BASED IMAGE RETRIEVAL

Content Based Image Retrieval (CBIR) techniques are becoming an essential requirement in the multimedia systems with the widespread use of internet, declining cost of storage devices and the exponential growth of un-annotated digital image information available in recent years. Therefore multi query systems have been used rather than a single query in order to bridge the semantic gaps and in order to understand user’s requirements. Moreover, query replacement algorithm has been used in the previous works in which user provides multiple images to the query image set referred as representative images. Feature vectors are extracted for each image in the representative image set and every image in the database. The centroid, Crep of the representative images is obtained by computing the mean of their feature vectors. Then every image in the representative image set is replaced with the same candidate image in the dataset one by one and new centroids are calculated for every replacement .The distance between each of the centroids resulting from the replacement and the representative image centroid Crep is calculated using Euclidean distance. The cumulative sum of these distances determines the similarity of the candidate image with the representative image set and is used for ranking the images. The smaller the distance, the similar will be the image with the representative image set. But it has some research gaps like it takes a lot of time to extract feature of each and every image from the database and compare our image with the database images and complexity as well as cost increases. So in our proposed work, the KNN algorithm is applied for classification of images in the database image set using the query images and the candidate images are reduced to images returned after classification mechanism which leads to decrease the execution time and reduce the number of iterations. Hence due to hybrid model of multi query and KNN, the effectiveness of image retrieval in CBIR system increases. The language used in this work is C /C++ with Open CV libraries and IDE is Visual studio 2015. The experimental results show that our method is more effective to improve the performance of the retrieval of images.

The Optimization of Query Processing in Seabase Cloud Databases based on CCEVP Model

A cloud database is a database usually installed on cloud computing software platforms. There are several methods for query processing in cloud databases. This study tried to optimize query processing in the SeaBase cloud database and reduce query processing time. This method used adaptability for optimization. This method was designed for cloud-based databases. The algorithm is composed of three components: 1) multi cloud query separator; 2) query similarity detector based on the execution plan; and 3) replacement policy. This method is implemented as a system for a fully object-oriented simulation. The system is added to the SeaBase as an agent. The evaluation result show that this method reduced response time by 1.9 percent.

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.

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.

Scalable and efficient processing of top-k multiple-type integrated queries

In this paper, we define a new class of queries, the top-k multiple-type integrated query (simply, top-k MULTI query). It deals with multiple data types and finds the information in the order of re...

Timestamp Embedding Query Stream Processing Engine

Background/Objectives: To improve the performance of the streaming data through RDF vocabulary and multi query optimization. Methods/Statistical Analysis: The scalability and performance of the query processor is improved with the use of native timestamp embedding query processor with the help of semantic approach. This offers vibrant, scalable services for continuous query processing. Continuous data is controlled through dynamic sliding window for incremental evaluation of streaming data. Static queries are processed continuously and all processing is done within main memory. Semantic sensor web is used to processor sensor data semantically by query processing engine and metadata will be stored as triple stores. Findings: The continuous stream of queries is properly handled by the dynamic sliding window and sent to the query optimizer for optimization. The triple formatted outcomes are warehoused into OWL, which stores and adopt the relational database format. The data conversion will be done by the data transformer and finally results will be realized as stream of data. In this proposed model, the RDF vocabulary has been redesigned by adding timestamps along with incoming triples. The classes and properties are depicted in terms of the triples (Subject S, Object O and Predicate P) and are designed as OWL. Data sets are split into smaller parts for continuous updates and collect them into massive data sets so as to obtain faster insertion rates. It takes less time to process temporal, complex and multi queries in proposed system. Since timestamp is attached as one of the object in the triples, it is easy to retrieve and execute timestamp embedding queries. The proposed system is appropriate for all types of queries and proves efficiency in execution time. Application/Improvement: Temporal, complex and multi join queries are executed and evaluated for weather monitoring system.

Coupling Materialized View Selection to Multi Query Optimization

Materialized views are queries whose results are stored and maintained in order to facilitate access to data in their underlying base tables of extremely large databases. Selecting the best materialized views for a given query workload is a hard problem. Studies on view selection have considered sharing common sub expressions and other multi-query optimization techniques. Multi-Query Optimization is a well-studied domain in traditional and advanced databases. It aims at optimizing a workload of queries by finding and reusing common sub-expression between queries. Finding the best shared expression is known as a NP-hard problem. The shared expressions usually identified by graph structure have been used to be candidate for materialized views. This shows the strong interdependency between the problems of materialized view selection (PVS) and multi query optimization (PMQO), since the PVS uses the graph structure of the PMQO. Exploring the existing works on PVS considering the interaction between PVS and PMQO figures two main categories of studies: (i) those considering the PMQO as a black box where the output is the graph and (ii) those preparing the graph to guide the materialized view selection process. In this category, the graph generation is based on individual query plans, an approach that does not scale, especially with the explosion of Big Data applications requiring large number of complex queries with high interaction. To ensure a scalable solution, this work proposes a new technique to generate a global processing plan without using individual plans by borrowing techniques used in the electronic design automation (EDA) domain. This paper first presents a rich state of art regarding the PVS and a classification of the most important existing work. Secondly, an analogy between the MQO problem and the EDA domain, in which large circuits are manipulated, is established. Thirdly, it proposes to model the problem with hypergraphs which are massively used to design and test integrated circuits. Fourthly, it proposes a deterministic algorithm to select materialized views using the global processing plan. Finally, experiments are conducted to show the scalability of our approach.

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.

Multi Queries Methods of the Chinese-English Bilingual Plagiarism Detection

Cross-language plagiarism detection identifies and extracts plagiarized text in a multilingual environment. In recent years, there has been a significant amount of work done involving English and European text. However, somewhat less attention has been paid to Asia languages. We compared a number of different strategies for Chinese-English bilingual plagiarism detection. We present methods for candidate document retrieval and compare four methods: (i) document keywords based, (ii) intrinsic plagiarism based, (iii) headers based, and (iv) machine translation queries. The results of our evaluation indicated that keywords based queries, the simplest and most efficient approach, gives acceptable results for newspaper articles. We also compared different percentage of keywords based query, and the results indicated that putting 50% keywords into queries can obtain the satisfied candidate documents set.

A novel and efficient ligand-based virtual screening approach using the HWZ scoring function and an enhanced shape-density model

In this work, we extend our previous ligand shape-based virtual screening approach by using the scoring function Hamza–Wei–Zhan (HWZ) score and an enhanced molecular shape-density model for the ligands. The performance of the method has been tested against the 40 targets in the Database of Useful Decoys and compared with the performance of our previous HWZ score method. The virtual screening results using the novel ligand shape-based approach demonstrated a favorable improvement (area under the receiver operator characteristics curve AUC = .89 ± .02) and effectiveness (hit rate HR1% = 53.0% ± 6.3 and HR10% = 71.1% ± 4.9). The comparison of the overall performance of our ligand shape-based method with the highest ligand shape-based virtual screening approach using the data fusion of multi queries showed that our strategy takes into account deeper the chemical information of the set of active ligands. Furthermore, the results indicated that our method are suitable for virtual screening and yields superior prediction accuracy than the other study derived from the data fusion using five queries. Therefore, our novel ligand shape-based screening method constitutes a robust and efficient approach to the 3D similarity screening of small compounds and open the door to a whole new approach to drug design by implementing the method in the structure-based virtual screening.

Polyline Spatial Join Evaluation Using Raster Approximation

The main subject of spatial joins is polygons and polylines. Typical polygons and polylines can occupy several Kbytes. Since approximations use much less space, the processing of spatial joins can be greatly improved by the use of filters that reduce the need for examining the exact geometry of spatial objects in order to find the intersecting ones. Candidate pairs of approximations of spatial objects are evaluated using such filters. As a result, three possible sets of answers are identified: the positive one, composed of intersecting pairs; the negative one, composed of non-intersecting pairs; and the inconclusive one, composed of the remaining pairs of candidates. There are many approximations designed for polygons, but few are suitable for approximating polylines. This paper presents a spatial join processor based on the multi step query processor (MSQP) architecture [24]. We have developed a new polyline approximation, named five color direction raster signature (5CDRS) [18]. It is used as the filter of MSQP’s second step. The performance of the join processor and the approximation was evaluated with real world data sets. The results showed that our approach, when compared to others presented in the literature, reduced the inconclusive answers by 29% in the average. Consequently, the need for retrieving the representation of polylines and carrying out exact geometry tests is reduced by the same factor. As the exact geometry test is the most time consuming step, we have noticed that the overall time is also reduced by 38% in the average. The disk accesses are reduced by 41% in the average.

유사한 색상과 질감영역을 이용한 객체기반 영상검색

Object-based image retrieval method is addressed. A new image segmentation algorithm and image comparing method between segmented objects are proposed. For image segmentation, color and texture features are extracted from each pixel in the image. These features we used as inputs into VQ (Vector Quantization) clustering method, which yields homogeneous objects in terns of color and texture. In this procedure, colors are quantized into a few dominant colors for simple representation and efficient retrieval. In retrieval case, two comparing schemes are proposed. Comparing between one query object and multi objects of a database image and comparing between multi query objects and multi objects of a database image are proposed. For fast retrieval, dominant object colors are key-indexed into database.

Efficient and extensible algorithms for multi query optimization

Complex queries are becoming commonplace, with the growing use of decision support systems. These complex queries often have a lot of common sub-expressions, either within a single query, or across multiple such queries run as a batch. Multiquery optimization aims at exploiting common sub-expressions to reduce evaluation cost. Multi-query optimization has hither-to been viewed as impractical, since earlier algorithms were exhaustive, and explore a doubly exponential search space. In this paper we demonstrate that multi-query optimization using heuristics is practical, and provides significant benefits. We propose three cost-based heuristic algorithms: Volcano-SH and Volcano-RU, which are based on simple modifications to the Volcano search strategy, and a greedy heuristic. Our greedy heuristic incorporates novel optimizations that improve efficiency greatly. Our algorithms are designed to be easily added to existing optimizers. We present a performance study comparing the algorithms, using workloads consisting of queries from the TPC-D benchmark. The study shows that our algorithms provide significant benefits over traditional optimization, at a very acceptable overhead in optimization time.