Query Mode Research Articles

Micromonosporaceae biosynthetic gene cluster diversity highlights the need for broad-spectrum investigations.

Investigations of the bacterial family Micromonosporaceae have enabled the development of secondary metabolites critical to human health. Historical investigation of bacterial families for natural product discovery has focused on terrestrial strains, where time-consuming isolation processes often lead to the rediscovery of known compounds. To investigate the secondary metabolite potential of marine-derived Micromonosporaceae , 38 strains were sequenced, assembled and analysed using antiSMASH and BiG-SLiCE. BiG-SLiCE contains a near-comprehensive dataset of approximately 1.2 million publicly available biosynthetic gene clusters from primarily terrestrial strains. Our marine-derived Micromonosporaceae were directly compared to BiG-SLiCE’s preprocessed database using BiG-SLiCE’s query mode; genetic diversity within our strains was uncovered using BiG-SCAPE and metric multidimensional scaling analysis. Our analysis of marine-derived Micromonosporaceae emphasizes the clear need for broader genomic investigations of marine strains to fully realize their potential as sources of new natural products.

Business Intelligence Based on Kimball Nine-Steps Methodology for Monitoring the Feasibility of Goods in Market

Consumer protection is of great concern to the Government of Indonesia through the supervision of goods on the market by the Department of Industry and Trade in each province. The Bali Provincial Office of Industry and Trade has transactional data from various data sources but has not been able to optimize it to support monitoring of goods circulating in the market. This research designs and implements a Data Warehouse-based Business Intelligence system using the Kimball Nine-Steps Methodology and Pentaho BI tools, to facilitate the storage and processing of goods data on the market, and monitor their feasibility. The results of this research show that the system can assist the Bali Province Industry and Trade Office in monitoring the feasibility of goods circulating in the market through the selection process for determining query priorities and query modes, as well as supporting decision-making processes and determining business strategies.

New trends in bibliometric APIs: A comparative analysis

The science of science practice requires the analysis of large and complex bibliometric data. Traditional data exporting from companies’ websites is not sufficient, so APIs are used to access a larger corpus. Therefore, this study aims not only to establish a taxonomy but also to offer a comparative analysis of 44 bibliographic APIs from various non-profit and commercial organizations, analyzing their characteristics and metadata with descriptive analysis, their possible bibliometric analyses, and the interoperability of the APIs across four different data categories: general, content, search, and query modes. The study found that Clarivate Analytics and Elsevier offer highly versatile APIs, while non-profit organizations, such as OpenCitations and OurResearch promote the Open Science philosophy. Most organizations offer free access to APIs for non-commercial purposes, but some have limitations on metadata retrieval. However, CrossRef, OpenCitations, or OpenAlex have no restrictions on the metadata retrieval. Co-author analysis using author names and bibliometric evaluation using citations are the types of analyses that can be done with the data provided by most APIs. DOI, PubMedID, and PMCID are the most versatile identifiers for extending metadata in the APIs. Semantic Scholar, Dimensions, ORCID, and Embase are the APIs that offer the most extensibility. Considering the obtained results, there is no single API that gathers all the information needed to perform any bibliometric analysis. Combining two or more APIs may be the most appropriate option to cover as much information as possible and enrich reports and analyses. This study contributes to advancing the understanding and use of APIs in research practice.

Free-form CAD model retrieval approach for engineering reuse based on local feature segmentation

For engineering applications, an innovative design can be developed by reusing and modifying existing models with similar features and manufacturing properties, and accurately searching CAD models has become a valuable knowledge acquisition technique in product design. Although there are many retrieval technologies, most methods focus on the global shape of models. In this paper, a CAD model retrieval framework based on local feature segmentation is proposed, offering users a free-form way to choose any subpart as a query from a given model. First, a novel model segmentation method based on the vertex-neighbor extension is proposed to divide a target CAD model into overlapping local features. The chosen subpart with arbitrary boundaries is also successfully described for retrieval by combining local features. Then, a composite descriptor considering both shapes and attribute characteristics is established, which is proved effective for distinguishing local features. Finally, the problem of matching free-form queries to CAD models in the dataset is transformed into descriptor set measurement and is implemented using the bag-of-word algorithm. The proposed model retrieval approach outperforms many existing approaches in matching performance and provides a user-friendly query mode.

Research and design of CRT-based homomorphic ciphertext database system

The cloud’s storage and query of private information have the cryptographic scholar due to the proliferation of cloud computing. In the traditional query mode, the private information stored in the cloud is at risk of being leaked. In order to solve this problem, a cloud ciphertext database system based on homomorphic encryption is a valid workaround. This paper presents a new cloud ciphertext database system model, which is based on the existing ciphertext database mode research and homomorphic properties. This paper also implements a ciphertext database system based on a CRT-based additive homomorphic scheme according to the model. Through theoretical analysis, the model is CPA-level safe and correct. The experimental results show that users can correctly query and download the data in the ciphertext database on the untrusted cloud server through the model, and it has efficiency advantages.

An Efficient Algorithm of Star Subgraph Queries on Urban Traffic Knowledge Graph

Knowledge graph has wide applications in the field of computer science. In the knowledge service environment, the information is large and explosive, and it is difficult to find knowledge of common phenomena. The urban traffic knowledge graph is a knowledge system that formally describes urban traffic concepts, entities and their interrelationships. It has great application potential in application scenarios such as user travel, route planning, and urban planning. This paper first defines the urban traffic knowledge graph and the star subgraph query of the urban traffic knowledge graph. Then, the road network data and trajectory data are collected to extract the urban traffic knowledge, and the urban traffic knowledge graph is constructed with this knowledge. Finally, a star subgraph query algorithm on the urban traffic knowledge graph is proposed. The discussion of the star subgraph query mode gives the corresponding application scenarios of our method in the urban traffic knowledge graph. Experimental results verify the performance advantages of this method.

Formation of the base and the automated system of the data integration about the industry

The expediency of forming the specialized database about industry and the system of their automated collection and usage were justified in this paper. This system allows to provide the information needs of the analysts and the scientists about the problems of industrial development. It provides the automated collection of the information for many countries of the world using any number of sources available as client-server resources on the Internet. The usage of modern data integration algorithms provides a diverse presentation of information, formats for its provision and frequency of updates. In addition, the advanced users of the system are provided with a wide range of options for creating search criteria and data acquisition format. The system is built as a client-server technology and is available for seamless integration with similar systems as a source and storage of information. In order to build a system, the analysis of existing in the world and popular in use statistical databases was carried out, their features, advantages and disadvantages were deаfined. It has been established that most of the existing statistical systems do not provide statistical data for Ukraine, and where they are, there is no detail data by type of economic activity and industry, and especially in industry. In addition, there are certain technical difficulties in working with data for users, the personalization of access is almost non-existent. The possibilities of the integrating existing databases with the systems that use information for modeling and forecasting both in query mode and especially in real time are rather limited. The structure of the statistical database about the development of industry has been formed, the indicators have been selected for its filling. The indicators were distributed by the sectors of the economy (the raw material production sector, the processing sector, the macrostatistics, other sectors). The sectors were disclosed by the key types of economic activity in accordance with the classification of economic activities in 2010 (CEA-2010), providing the predominant part of the creation of the added value of the economy of Ukraine. Each specific type of economic activity is disclosed by groups of industries and industries. In addition, the formation of the statistical database was carried out on the basis of assigning to each of the indicators other characteristics necessary for the automation: code, units of measurement, period, base (distribution of the indicators into the separate groups for the possibility of modeling), source (the statistical bases and sources from which the indicators and the data were collected). The modern relational database was used to store information, which allows solving optimization issues for working with the most powerful, but not yet large data, taking into account the features of wide data, allows horizontal and vertical scaling, including in the PostgreSQL open source database system. The methodology for the formation and the technology for filling the statistical database automatically from a large number of sources, the access mode to which is set by the system configuration parameters has been created. The technology has been developed for data migrations available in flexible formats, including text, in particular Excel. The implementation of the developed automated information system integration of industrial statistics allows you to select the necessary indicators for the analysis of economic processes in industry, use the statistical data collected in a single information space for 130 countries of the world (including Ukraine) for conducting scientific researches, building economic and mathematical models and making managerial

Wright about Wight: A dialect glossary of the Isle of Wight based on EDD Online

AbstractDescribing specific dialect areas in terms of their lexis is an attractive idea now that the latest version of the English Dialect Dictionary Online (EDD Online 3.0, 2019) allows for quick and easy lexical retrievals of English dialect words of the Late Modern English period. This paper uses the Isle of Wight (I.W.) as a test case for putting such an idea into practice. The 137 words uniquely attributed in the EDD to I.W. are analyzed and interpreted in relation to the 1500-odd words used on I.W. alongside other areas of the UK. The paper informs the reader of the available query modes and discusses their pros and cons, quantifying and mapping the different numbers of isolated words in use on I.W. versus those unique to other English counties. The larger number of words that the island shared with the counties of the “mainland” will likewise be considered, thus allowing for first steps towards a “dialectometrical” analysis. The findings are related to the historical background of I.W., particularly in the 18th and 19th centuries.

Analysis and Identification of International Tourist Visits to Indonesia Based on Data Warehouse Implementation

Foreign tourist visits to Indonesia play an important role in the economic growth of the tourism sector. In increasing the interest of visitors, development resources are needed as a tourism sector. The development carried out must be carried out optimally with the growth trend of tourist visits so that development is right on target, effective and efficient. This study aims to visit foreign tourists to Indonesia using the implementation of a data warehouse. The dataset used is data on foreign tourist arrivals by nationality in 2021 sourced from the Central Statistics Agency (BPS). This is done to determine the interest and potential of tourism to support the country's economy. The steps taken in this analysis and identification are Choosing the Process, Choosing the Grain, Identifying and confirming the dimension, Choosing The Facts, Rounding out the dimension table, Choosing the duration of database, Tracking Slowly changing Dimensions, Deciding the query priorities and the query mode, and the last Storing pre-calculation in the fact information. The result can be seen Foreign Tourism Visits in Indonesia using the Application of OLAP Data Warehouse for analysis. So that in 2021 it can be analyzed that monthly tourist visits tend to be stable with the periods of July-August and January-December increasing. These results in the future can be used as a policy reference in increasing tourist visits to foreign tourist targets in certain countries. This is done to determine the interest and potential of tourism to support the country's economy through the tourism sector

A blockchain index structure based on subchain query

Blockchain technology has the characteristics of decentralization and tamper resistance, which can store data safely and reduce the cost of trust effectively. However, the existing blockchain system has weak performance in data management, and only supports traversal queries with transaction hashes as keywords. The query method based on the account transaction trace chain (ATTC) improves the query efficiency of historical transactions of the account. However, the efficiency of querying accounts with longer transaction chains has not been effectively improved. Given the inefficiency and single method of the ATTC index in the query, we propose a subchain-based account transaction chain (SCATC) index structure. First, the account transaction chain is divided into subchains, and the last block of each subchain is connected by a hash pointer. The block-by-block query mode in ATTC is converted to the subchain-by-subchain query mode, which shortens the query path. Multiple transactions of the same account in the same block are merged and stored, which simplifies the construction cost of the index and saves storage resources. then, the construction algorithm and query algorithm is given for the SCATC index structure. Simulation analysis shows that the SCATC index structure significantly improves query efficiency.

Feature Selection and Composition Using PyOracle

A system is described which uses the Audio Oracle algorithm for music analysis and machine improvisation. Some improvements on previous Factor Oracle-based systems are presented, including automatic model calibration based on measures from Music Information Dynamics, facilities for compositional structuring and automation, and an audio-based query mode which uses the input signal to influence the output of the generative system.

Database Construction in Computer Audit under Big Data Environment

In the information age, the explosive growth of data forces users to deliver the data to the database service provider to manage. This paper mainly studies the database construction of computer audit under the environment of big data. This paper proposes a queryable database encryption storage scheme and a searchable multi-keyword encryption scheme under distributed environment. In this paper, the non-relational database is used in distributed storage in the form of key-value to improve the query performance. For distributed database, this paper proposes a queryable database encryption scheme. In this scheme, there are two encryption query modes: exact matching mode and range matching mode. Users encrypt and store data to the corresponding server nodes in these two ways, which can support rich queries. In the process of query, the user generates the corresponding query password for each server node, and then the parallel query can be realized. While protecting data privacy, the performance of the scheme is also well guaranteed.

BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters.

BackgroundGenome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools are hindered by a bottleneck caused by the expensive network-based approach used to group these BGCs into gene cluster families (GCFs).ResultsHere, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes within 10 days on a typical 36-core CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a “query mode” that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration.ConclusionsBiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.

Research on Routing Strategy in Cluster Deduplication System

A cluster deduplication system can coordinate the work of multiple nodes, which can better alleviate the disk index bottleneck existing in the large-scale data backup system. However, there is a problem of isolated islands of information among nodes during data deduplication. When the servers use the query mode to route data, a large amount of system overhead is required to ensure a high deduplication rate and low throughput rate. At the same time, while the servers cannot obtain a higher deduplication rate if the servers adopt the stateless routing method. Data routing strategy can greatly affect the overall performance of the system. The concept of data frequency is proposed in this paper, and the classified routing strategy is designed. In the metadata server, a byte-shaped Bloom filter for recording the occurrence frequency of data blocks is maintained to record the occurrence frequency of data blocks. The values in the Bloom filter are counted. Then the frequency of the data blocks is compared with the configured threshold value to determine whether the data is “hot data”. We use stateful routing to send “clod data” to the storage nodes and use stateless routing to send the hot data to the storage nodes. Experimental results show that the classifying routing algorithm based on the frequency of data can greatly reduce the overhead of the system while guaranteeing the deduplication rate of the deduplication system as well as improve system throughput and real-time processing capabilities. Compared with the fully stateful routing scheme, our method only loses less than 2% of the deduplication rate, which reduces the communication query overhead by more than 25% and improves the real-time processing capability of the storage system.

Exciting-Inhibition Network for Person Reidentification in Internet of Things

Person reidentification (re-ID), which aims at recognizing the pedestrians captured by multiple nonoverlapping cameras, has attracted more interest due to its significant and potential application in the Internet of Things like intelligent visual surveillance. However, person reID is still a challenging problem in the situations of various pose, similar appearances, partial occlusion, etc. To handle these obstacles, in this article, we investigate an innovative exciting-inhibition network (EINet) that is a two-branch network composed of the exciting branch and the inhibition branch. The channel-spatial attention block that recalibrates the relationship between channels and highlights features at different spatial positions is used in the exciting branch. A novel Soft Batch DropBlock that randomly selects a continuous region of the intermediate feature maps at the same location is applied in the inhibition branch to inhibit the trivial by an inhibitive mask and reinforce learning the remaining regions. We integrate the comprehensive features from both branches for evaluation and show the performance of EINet intuitively using the visualization method. Abundant experiments demonstrate the state-of-the-art performance by comparing with the previous methods on three popular person re-ID benchmarks. For example, our method obtains 95.64% Rank-1 and 88.75% mean average precision (mAP) on Market-1501, and 77.00% Rank-1 and 74.51% mAP on CUHK03-Detect in the single query mode, respectively.

Massive Ship Fault Data Retrieval Algorithm Supporting Complex Query in Cloud Computing

Lou, H., 2019. Massive ship fault data retrieval algorithm supporting complex query in cloud computing. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1013–1018. Coconut Creek (Florida), ISSN 0749-0208.Aiming at the problems of the current retrieval algorithm for massive ship fault data retrieval in cloud computing, which does not support complex query, large retrieval cost and low precision, a massive ship fault data retrieval algorithm supporting complex query in cloud computing based on query performance prediction is proposed. Introducing the Lagrangian algorithm to preprocess the ship fault data information; combining the attribute values of the ship fault data and the context to calculate the similarity between different attributes, and identifying the same attribute according to the similarity; The attribute recognition result is queried by using multiple retrieval models, and the model complex query request is converted into a one-dimensional query key value, and the query key values obtained by the plurality of models are predicted, and the search result with the optimal prediction performance of the complex query mode is used as the final result. The experimental results show that the proposed algorithm has good retrieval accuracy in complex query mode, and it has less overhead than the current search algorithm.

A novel feature transform framework using deep neural network for multimodal floor plan retrieval

In recent past, there has been a steep increase in the use of online platforms for the search of desired products. Real estate industry is no exception and has started initiating rent/sale of houses through online platforms. In this paper, we propose a deep neural network framework to facilitate automatic search of homes based on their floor plans. The salient features of this framework are that the query can be either an image (existing floor plan) or a sketch through a sketch pad interface. Our proposed framework automatically determines the type of query (image or sketch) and retrieves similar floor plan images from the database. The critical contributions of our proposed approach are: (1) a novel unified floor plan retrieval framework using multimodal query, i.e., an intuitive and convenient sketch query mode as well as a query by example mode ; (2) a conjunction of autoencoder, Cyclic GAN and CNN for the task of domain mapping and floor plan image retrieval. We have reported results of extensive experimentation and comparison with baseline results to establish the effectiveness of our approach.

Quantitative similarity calculation method for trajectory-directed line using sketch retrieval

Moving objects may have various kinds of topological events when they move relative to a directed line, such as moving in/out, folding back and stopping. This research develops a trajectory-directed line query mode using sketch-based retrieval in order to realize the query of topological relationship between a trajectory and a directed line. The research also proposes a quantitative similarity calculation method for trajectory-directed line. The sketched stroke-directed line is used as input query condition and is described as a sequence of key points for semantic association. The trajectory-directed line to be queried is also described as a sequence of key points. The time sequence distance metric is used to calculate the similarity between them. For the trajectory-directed line with different number of key points, the similarity calculation is carried out using two kinds of distance matching methods. The results show that the method is simple and feasible, and can realize the quantitative query of trajectory data.

Agronomic Linked Data (AgroLD): A knowledge-based system to enable integrative biology in agronomy.

Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge to adopt an integrative research approach. We are facing an urgent need to effectively integrate and assimilate complementary datasets to understand the biological system as a whole. The Semantic Web offers technologies for the integration of heterogeneous data and their transformation into explicit knowledge thanks to ontologies. We have developed the Agronomic Linked Data (AgroLD– www.agrold.org), a knowledge-based system relying on Semantic Web technologies and exploiting standard domain ontologies, to integrate data about plant species of high interest for the plant science community e.g., rice, wheat, arabidopsis. We present some integration results of the project, which initially focused on genomics, proteomics and phenomics. AgroLD is now an RDF (Resource Description Format) knowledge base of 100M triples created by annotating and integrating more than 50 datasets coming from 10 data sources–such as Gramene.org and TropGeneDB–with 10 ontologies–such as the Gene Ontology and Plant Trait Ontology. Our evaluation results show users appreciate the multiple query modes which support different use cases. AgroLD’s objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to the implication of genes/proteins in, for instances, plant disease resistance or high yield traits. We expect the resolution of these questions to facilitate the formulation of new scientific hypotheses to be validated with a knowledge-oriented approach.

Personalized file data query matching method based on SOA

ABSTRACT In view of the problem that XML wildcard query processing needs to read all the element tags in the file into the memory and the matching efficiency is low, a custom personalized file data query matching process (CPQASOA) based on SOA is proposed. The algorithm uses bottom-up pattern matching based on effective filtering strategies. It converts wildcards into A-D relationships and hierarchical information constraints, solves traditional wildcard matching problems, avoids multiple scan query modes and improves query speed. Theoretical analysis and experimental results show that the query efficiency of the algorithm is obviously better than that of existing algorithms.