Database Access Methods Research Articles

Best practices of testing database manipulation code

Software testing enables development teams to maintain the quality of a software system while it evolves. The database manipulation code requires special attention in this context. However, it is often neglected and suffers from software maintenance problems.In this paper, we study the current state-of-the-practice in testing database manipulation code. We first analysed the tests of 72 open-source projects to gain insight into the coverage of database access code. The database was poorly tested: 46% of the projects did not cover with tests half of their database access methods, and 33% did not cover the database code at all. This poor coverage motivated us to study developers’ challenges and best practices. (i) First, we analysed 532 questions on Stack Exchange sites and deduced a taxonomy of issues. Developers mostly looked for general best practices to test database access code. Their technical questions were related to database management, mocking, parallelisation, or framework/tool usage. (ii) Next, we examined the answers to these questions. We manually labelled 598 answers to 255 questions. We distinguished 363 solutions and organised them in a taxonomy of best practices. Most of the suggestions considered the testing environment and recommended various tools or configurations. The second largest category was database management, where many addressed database initialisation and clean-up between tests. Other categories pertained to code structure or design, concepts, performance, processes, test characteristics, test code, and mocking. We illustrate the two taxonomies through intriguing examples.

Local Climate Zone classification for climate-based urban planning using Landsat 8 Imagery (A case study in Yogyakarta Urban Area)

Urbanization of the last decade contributed to the urban morphological changes in Yogyakarta Urban Area (YUA). These conditions have an impact on local climate zone variations. There is an urgent need for enhanced climate-based urban planning. This study aims to: 1) Classify the Local Climate Zone (LCZ) in YUA using the World Urban Database Access and Portal Tool (WUDAPT) method, and 2) develop climate-based urban planning recommendations of the classification results. WUDAPT method used in this research based on the concept of LCZ framework by utilizing Landsat 8 imagery. LCZ classification of the whole area is determined using random forest classifier algorithm by SAGA GIS. The results of this study indicate that there are 12 LCZs in YUA, consisting of 6 types of LCZ based on building type and 6 LCZs based on land cover type. The highest increase of LST are in LCZ 3 and LCZ 5. Consequently, there’s a change in diurnal temperature from low to medium. Therefore, the thermal comfort aspect define by four components of LCZ is important in YUA urban planning strategy.

3D NEAREST NEIGHBOUR SEARCH USING A CLUSTERED HIERARCHICAL TREE STRUCTURE

Abstract. Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

3D NEAREST NEIGHBOUR SEARCH USING A CLUSTERED HIERARCHICAL TREE STRUCTURE

Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics

BackgroundIn spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography–mass spectrometry (LC–MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases.DescriptionHere we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC–MS accurate mass data enabled the identity of an unknown peak to be confidently predicted.ConclusionsMINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures.Graphical abstractMINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0087-1) contains supplementary material, which is available to authorized users.

Design and Implementation of University Archives Management System Based on .Net Technology

ASP.NET and Web database are integrated to develop a B/S mode-based university archives management system so as to improve the efficiency and security of archives management system. Meanwhile, detailed analysis and study are made on related technologies such as systems demand analysis, overall design, database access method, and precautions for data security.

Realization of Deep Web Database Optimized Access Based on Correlation Feature Rule

Realization of big Deep Web database optimized access with low sample deviation was researched. With the sharp increasing of the storage of Deep Web database geometrically, the problem of optimized access to the database was becoming difficult. On the basis of correlation feature rule extraction, an improved optimized access method for the Deep Web database was proposed based on Graph Model Sample process and correlation rule. The access was realized by computer simulation. The access process was started with the arbitrary effective result as the access starting line. The access result was returned, and the records were obtained in the returned web page. The next query and access was implemented with the local sample database for the nest access start line. The method avoided the effects of the query interface properties. And the Graph Model Sample method could overcome the limitation of the query interface properties. Also the correlation feature extraction algorithm was researched, and it was used as the access rule in the database for the realization of optimized and efficient query. The simulation result shows that the sample deviation is stable with the value about 10% for the simulated Deep Web database. The relevant sample deviation value is trended to convergence and is near to the real value with the increasing of the access number. According to the realistic Web database, the sample bias estimation mean value is about 21% which is higher than the simulated database. Two typical Deep Web databases are taken as the researching objects, with the new method, and the access bias is lower than another Web database which without using the proposed method, result shows nice performance of the proposed optimized database access method in application.

Agent-based Mobile Data Caching Strategies Using Data Significance

Mobile computing is currently becoming more common. This trend emphasizes the necessity of accessing database systems on fixed networks from mobile platforms via wireless networks. However, it is not an appropriate way that applies the database access methods for traditional computing onto mobile computing because of their essential restrictions. In this paper, we suggest an agent-based mobile data access model and also two functions calculating data significance scores to choose suitable data items for cache replacement and coherence policies. These functions synthetically reflect access term, access frequency and tendency, update frequency and tendency, and data item size distribution. As the result of simulation experiment, our policies outperform Least Recently Used LRU, LRU Inverses Frequency LIX, Stretch Access-rate Inverse Update-frequency SAIU policies in aspects of decrement of access latency, improvement of cache byte hit ratio, and decrease of cache byte pollution ratio.

Indexing in-network trajectory flows

Indexing moving objects (MO) is a hot topic in the field of moving objects databases since many years. An impressive number of access methods have been proposed to optimize the processing of MO-related queries. Several methods have focused on spatio-temporal range queries, which represent the foundation of MO trajectory queries. Surprisingly, only a few of them consider that the objects movements are constrained. This is an important aspect for several reasons ranging from better capturing the relationship between the trajectory and the network space to more accurate trajectory representation with lower storage requirements. In this paper, we propose T-PARINET, an access method to efficiently retrieve the trajectories of objects moving in networks. T-PARINET is designed for continuous indexing of trajectory data flows. The cornerstone of T-PARINET is PARINET, an efficient index for historical trajectory data. The structure of PARINET is based on a combination of graph partitioning and a set of composite B+-tree local indexes. Because the network can be modeled using graphs, the partitioning of the trajectory data makes use of graph partitioning theory and can be tuned for a given query load and a given data distribution in the network space. The tuning process is built on a good quality cost model that is supplied with PARINET. The advantage of having a cost model is twofold; it allows a better integration of the index into the query optimizer of any DBMS, and it permits tuning the index structure for better performance. The tuning process can be performed before the index creation in the case of historical data or online in the case of indexing data flows. In fact, massive online updates can degrade the index quality, which can be measured by the cost model. We propose a specific maintenance process that results into T-PARINET. We study different types of queries and provide an optimized configuration for several scenarios. T-PARINET can easily be integrated into any RDBMS, which is an essential asset particularly for industrial or commercial applications. The experimental evaluation under an off-the-shelf DBMS shows that our method is robust. It also significantly outperforms the reference R-tree-based access methods for in-network trajectory databases.

모바일 컴퓨팅 환경에서 데이터의 중요도에 기반한 캐시 교체와 일관성 유지

Recently, mobile computing environments are becoming rapidly common. This trend emphasizes the necessity of accessing database systems on fixed networks from mobile platforms via wireless networks. However, it is not an appropriate way that applies the database access methods for traditional computing environments to mobile computing environments because of their essential restrictions. This paper suggests a new agent-based mobile database access model and also two functions calculating data significance scores to choose suitable data items for cache replacement and coherence policies. These functions synthetically reflect access term, access frequency and tendency, update frequency and tendency, and data item size distribution. As the result of simulation experiment, our policies outperform LRU, LIX, and SAIU policies in aspects of decrement of access latency, improvement of cache byte hit ratio, and decrease of cache byte pollution ratio.

Personalized video similarity measure

As an effective technique to manage and explore large scale of video collections, personalized video search has received great attentions in recent years. One of the key problems in the related technique development is how to design and evaluate the similarity measures. Most of the existing approaches simply adopt traditional Euclidean distance or its variants. Consequently, they generally suffer from two main disadvantages: (1) low effectiveness--retrieval accuracy is poor. One of main reasons is that very little research has been carried out on designing an effective fusion scheme for integrating multimodal information (e.g., text, audio and visual) from video sequences and (2) poor scalability--development process of the video similarity metrics is largely disconnected from that of the relevant database access methods (indexing structures). This article reports a new distance metric called personalized video distance to effectively fuse information about individual preference and multimodal properties into a compact signature. Moreover, a novel hashing-based indexing structure has been designed to facilitate fast retrieval process and better scalability. A set of comprehensive empirical studies have been carried out based on two large video test collections and carefully designed queries with different complexities. We observe significant improvements over the existing techniques on various aspects.

IndexPro: An efficient protein indexing system based on knot theory

The proteins, with complex three dimensional structures, are traditionally time consuming to observe and work with. In this paper, we apply knot theory to represent proteins as an easily extensible numerical model, an R30. The model can not only be indexed efficiently with some commonly used spatial access methods in databases, e.g., the R* tree and the M tree, but can also be extended by any feature which can be quantified. We construct the system IndexPro and design several experiments to evaluate its performance. As the experimental results show, our system can correctly classify proteins at a satisfactory success rate. Moreover, with the help of the indexes, our system can operate tens of thousands of times faster than existing systems, and scale quite well as the number of proteins grow.

Analysis of the clustering properties of the Hilbert space-filling curve

Several schemes for the linear mapping of a multidimensional space have been proposed for various applications, such as access methods for spatio-temporal databases and image compression. In these applications, one of the most desired properties from such linear mappings is clustering, which means the locality between objects in the multidimensional space being preserved in the linear space. It is widely believed that the Hilbert space-filling curve achieves the best clustering (Abel and Mark, 1990; Jagadish, 1990). We analyze the clustering property of the Hilbert space-filling curve by deriving closed-form formulas for the number of clusters in a given query region of an arbitrary shape (e.g., polygons and polyhedra). Both the asymptotic solution for the general case and the exact solution for a special case generalize previous work. They agree with the empirical results that the number of clusters depends on the hypersurface area of the query region and not on its hypervolume. We also show that the Hilbert curve achieves better clustering than the z curve. From a practical point of view, the formulas given provide a simple measure that can be used to predict the required disk access behaviors and, hence, the total access time.

Discovering structural association of semistructured data

Many semistructured objects are similarly, though not identically structured. We study the problem of discovering typical substructures of a collection of semistructured objects. The discovered structures can serve the following purposes: 1) the table-of-contents for gaining general information of a source, 2) a road map for browsing and querying information sources, 3) a basis for clustering documents, 4) partial schemas for providing standard database access methods, and 5) user/customer interests and browsing patterns. The discovery task is affected by structural features of semistructured data in a nontrivial way and traditional data mining frameworks are inapplicable. We define this discovery problem and propose a solution.

Indexing bitemporal databases as points

A bitemporal database supports both the valid and transaction time dimensions. Records in such a database can be viewed as a rectangle in a 2-dimensional space (corresponding to the valid and transaction time dimensions). Hence, a spatial access method can be employed to facilitate speedy retrieval of the database. In this paper, we re-examine the issue of designing efficient access methods for bitemporal databases. In particular, we transform a record into a point in a multi-dimensional space, where the valid time and transaction time are each mapped to a 2-dimensional coordinate. A temporal selection operation can then be implemented as a region search operation. This allows us to tap into the many point access methods that are commercially available without modification. We implemented and evaluated three R-tree based methods on key-range time-slice queries: the naive Point R-tree, the Dual Point R-tree and the Dual Spatial R-tree. Our experimental results show that while the simple Point R-tree is inferior to the Dual Spatial R-tree, the Dual Point R-tree has the best performance of the three.

Designing access methods for bitemporal databases

By supporting the valid and transaction time dimensions, bitemporal databases represent reality more accurately than conventional databases. The authors examine the issues involved in designing efficient access methods for bitemporal databases, and propose the partial-persistence and the double-tree methodologies. The partial-persistence methodology reduces bitemporal queries to partial persistence problems for which an efficient access method is then designed. The double-tree methodology "sees" each bitemporal data object as consisting of two intervals (a valid-time and a transaction-time interval) and divides objects into two categories according to whether the right endpoint of the transaction time interval is already known. A common characteristic of both methodologies is that they take into account the properties of each time dimension. Their performance is compared with a straightforward approach that "sees" the intervals associated with a bitemporal object as composing one rectangle, which is stored in a single multidimensional access method. Given that some limited additional space is available, the experimental results show that the partial-persistence methodology provides the best overall performance, especially for transaction timeslice queries. For those applications that require ready, off-the-shelf, access methods, the double-tree methodology is a good alternative.

Efficient access methods for image databases

In the previous approaches to retrieve a symbolic picture from an image database which contains multiple symbolic pictures, only 9 spatial relationships that are represented in 2D strings or 9DLT matrices were considered. In this paper, we propose two efficient access methods for image databases, which can handle 169 spatial relationships. In the first proposed method, each record signature is represented by 26 bits and 26-bit strings of size N, where N > 0. In the second proposed method, each record signature is represented by 26 bits and 26 products of prime numbers. Given the same image databases, the same query picture and the same hash functions, the ratio of the false match in our strategy can be smaller than that of the previous approaches, where a false match is that a record signature matches a query signature but the corresponding record does not satisfy the query.

A Strategy for Database Interoperation

To realize the full potential of biological databases (DBs) requires more than the interactive, hypertext flavor of database interoperation that is now so popular in the bioinformatics community. Interoperation based on declarative queries to multiple network-accessible databases will support analyses and investigations that are orders of magnitude faster and more powerful than what can be accomplished through interactive navigation. I present a vision of the capabilities that a query-based interoperation infrastructure should provide, and identify assumptions underlying, and requirements of, this vision. I then propose an architecture for query-based interoperation that includes a number of novel components of an information infrastructure for molecular biology. These components include a knowledge base that describes relationships among the conceptualizations used in different biological databases, a module that can determine the DBs that are relevant to a particular query, a module that can translate a query and its results from one conceptualization to another, a collection of DB drivers that provide uniform physical access to different database management systems, a suite of translators that can interconvert among different database schema languages, and a database that describes the network location and access methods for biological databases. A number of the components are translators that bridge the heterogeneities that exist between biological DBs at several different levels, including the conceptual level, the data model, the query language, and data formats.

ADMS: a testbed for incremental access methods

ADMS is an advanced database management system developed-to experiment with incremental access methods for large and distributed databases. It has been developed over the past eight years at the University of Maryland. The paper provides an overview of ADMS, and describes its capabilities and the performance attained by its incremental access methods. This paper also describes an enhanced client-server architecture that allows an incremental gateway access to multiple heterogeneous commercial database management systems. >

Spatial database access methods

In the discussion of research issues in spatial databases (SIGMOD Record vol. 19, no. 4, Dec 1990) we stated the need for a robust framework for analytical comparison of a broad range of spatial access methods. The utility of such a comparison, even of very closely related access methods, was shown in [FALO87]. A necessary precondition for a meaningful analytical comparison is the existence of strong analytical results for individual access methods. In the following paper, Salzberg and Lomet take the worst case analytical results on fan-out and average storage utilization they obtained for their hB-tree [LOME89,LOME90] and extend the analysis to another robust method, Z-order encoding [OREN84]. We think this paper is a start on the comparative assessment of access methods based on analytical results. We hope to see future work extend the framework beyond worst case analysis, and to other access methods as well.