Top PDF Logical Querying of Relational Databases

Logical Querying of Relational Databases

Logical Querying of Relational Databases

There are advantages. Evaluating expressions and functional programming has already given us the support for a declarative way of parsing collections of objects. Since relational databases cease way to noSQL ones, we have to discover a good substitute for SQL language. Beginning with Java 8 lambda expressions, streams and method references, we have to search no more...

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Storing, Querying and Validating Fuzzy XML
          Data in Relational Database

Storing, Querying and Validating Fuzzy XML Data in Relational Database

Abstract— Information ambiguity and uncertainty are the major issues in real real-world scenario and for resolving these issues fuzzy data management has been incorporated in different database management systems in different ways. Even though there is much literature on XML-to-relational storage, few of these have given satisfactory solutions to the given problem of storing fuzzy XML data in RDBMs. In this paper, we attempt to present yet another technique for storing and querying fuzzy XML data in relational databases. The content XML document is retrieved using XPath. It views the XML document as a tree and uses one table that stores both the information contained in the nodes as well as the structure of the tree. We convert the XML tree representation into a table form using LOAD XML statement. We use SQL for query purposes. Also we can use crisp as well as fuzzy data for input in queries. We can also validate the XML input data using some unique key. It is a pre-defined key given at beginning of XML code. Further, we also propose a generic technique to convert path expression queries into SQL for processing XML queries.
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Representing and querying disease networks using graph databases

Representing and querying disease networks using graph databases

High volume datasets are being routinely generated in systems biomedicine. An on- going concern for the interpretation of these data is that biological information has a challenging mix of complexity and rich semantics. Modelling of data in graph database ideally are guided by the same principles as those used for ontology design. In a way, the ontology may play the same role as a schema plays in a relational database by defin- ing a valid set of relationships and/or properties that can exist for particular entities or their combinations. However, for this application case we have elected not to explore this topic in great detail in order to keep focus firmly on the applied aspects of man- aging biomedical data using the Neo4j database and to present it at a level accessible to a more general audience. For completeness, we would like to point out that from the ontology-centric data modelling perspective a Neo4j graph can be thought of as a Table 3 Gene symbols and corresponding UniProt identifiers, (resolved via the “ Retrieve/ID mapping ” tool), for the core circadian components
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Querying Large Databases

Querying Large Databases

This paper investigates two approaches to improving query times on large relational databases. The first technique cap- italizes on the knowledge of a database’s structures and properties one typically has. This technique can execute some queries exactly in a constant, bounded amount of time. When this technique cannot be used to exactly execute a query we show how it can still be used to drastically lower the run-time on the query while getting a good approxi- mation of the exact result. We also discuss the complexity of deciding whether a query is evaluable in this way, both theoretically and practically. The second approach approx- imates aggregate queries by incorporating only part of the data, rather than all of the data the query pertains to. We briefly investigate an established method of sampling a ran- dom subset of the data, and then a newer method which par- tially reads every tuple and puts deterministic error bounds on the results.
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Integrating analytics with relational databases

Integrating analytics with relational databases

DuckDB’s optimizer performs join order optimization using dynamic program- ming [57] with a greedy fallback for complex join graphs [61]. It performs flattening of arbitrary subqueries as described in Nuemann et al. [59]. In addition, there are a set of rewrite rules that simplify the expression tree, by performing e.g. common subexpression elimination and constant folding. Cardinality estimation is done using a combination of samples and HyperLogLog. The result of this process is the optimized logical plan for the query. The physical planner transforms the logical plan into the physical plan, selecting suitable implementations where applicable. For example, a scan may decide to use an existing index instead of scanning the base tables based on selectivity estimates, or switch between a hash join or merge join depending on the join predicates.
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KEYWORD SEARCH IN RELATIONAL DATABASES

KEYWORD SEARCH IN RELATIONAL DATABASES

Keyword searching is an effective method for finding information in any computerized database. It can be classified into two types, one is schema based keyword search and other is graph based key word search. Keyword search has been applied to retrieve useful data in documents, texts, graphs, and even relational databases. In Relational keyword search(R- KWS), the basic unit of information is a tuple/record. In contrast to Keyword search on documents, results in Relational keyword search cannot simply be found by inspecting units of information (records) individually. Instead, results have to be constructed by joining tuples. R-KWS has benefits over SQL queries. First, it frees the user from having to study a database schema. Second, R-KWS allows querying for terms in unknown locations (tables/attributes). Finally, a single R-KWS query replaces numerous complex SQL statements. Keyword search can be classified into two types. One is schema based approach, other is graph based approach.
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TEXT BOOK Database System Concepts 6e Korth

TEXT BOOK Database System Concepts 6e Korth

• 1980s: Although academically interesting, the relational model was not used in practice initially, because of its perceived performance disadvantages; rela- tional databases could not match the performance of existing network and hi- erarchical databases. That changed with System R, a groundbreaking project at IBM Research that developed techniques for the construction of an efficient relational database system. Excellent overviews of System R are provided by Astrahan et al. [1976] and Chamberlin et al. [1981]. The fully functional Sys- tem R prototype led to IBM ’s first relational database product, SQL/DS . At the same time, the Ingres system was being developed at the University of California at Berkeley. It led to a commercial product of the same name. Ini- tial commercial relational database systems, such as IBM DB2 , Oracle, Ingres, and DEC Rdb, played a major role in advancing techniques for efficient pro- cessing of declarative queries. By the early 1980s, relational databases had become competitive with network and hierarchical database systems even in the area of performance. Relational databases were so easy to use that they eventually replaced network and hierarchical databases; programmers using such databases were forced to deal with many low-level implementation de- tails, and had to code their queries in a procedural fashion. Most importantly, they had to keep efficiency in mind when designing their programs, which involved a lot of effort. In contrast, in a relational database, almost all these low-level tasks are carried out automatically by the database, leaving the programmer free to work at a logical level. Since attaining dominance in the 1980s, the relational model has reigned supreme among data models.
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Querying Temporal Databases Using Controlled Natural Language

Querying Temporal Databases Using Controlled Natural Language

Recent years have shown a surge in interest in temporal database systems, which allow users to store time-dependent information. We present a novel controlled natural language interface to temporal databases, based on translating nat- ural language questions into SQL/Temporal, a temporal database query language. The syn- tactic analysis is done using the Type-Logical Grammar framework, highlighting its utility not only as a theoretical framework but also as a practical tool. The semantic analysis is done using a novel theory of the semantics of tempo- ral questions, focusing on the role of temporal preposition phrases rather than the more tradi- tional focus on tense and aspect. Our transla- tion method is considerably simpler than pre- vious attempts in this direction. We present a prototype software implementation.
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Genetic Algorithm and Fuzzy Logic Based Flexible Querying in Databases

Genetic Algorithm and Fuzzy Logic Based Flexible Querying in Databases

Usage of the fuzzy set theory in the query systems started in the second half of 1970s. After its first usage by Tahani, the fuzzy queries emerge over time with different methods [5]. In 1980, Kacprzyk and Ziolkowski developed a proto-type software on a Polish made, 16-bit minicomputer with MERA-400 operating system named FQuery1 as an addition to DB-83 database [6]. This software graded the records in the database with the membership function. With this software both basic queries and fuzzy queries could be made as discussed by Asar [7]. There were also studies on fuzzy logic and relational databases in 1980s. Zvieli and Chen proposed fuzzy logic based ER (Entity-Relationship) model [8]. Another proposed model related with the fuzzy databases is the GEFRED model [9]. It was modeled on relational databases based on probability and fuzzy logic and also constitutes a foundation for further studies [10], [11]. In the 2000s, Chaudhry, Moyne and Rundenstain developed the design method of probability based fuzzy relational databases. This method is a model based on generating fuzzy fields by probability related calculations [12]. Besides these, models such as Prade-Testemale, Umano-Fukami, Buckles-Petry and Zemankova-Kaendel Model have also been developed [13].
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Comparison between relational database and XML in querying motif sequence

Comparison between relational database and XML in querying motif sequence

Protein structure prediction is the process which requires scientists to perform a variety of searching motif sequence and secondary structure procedures in order to answers their analytical result. To perform these procedures, scientists need to use variety types of sequence motif data from databases which are available from bioinformatics web sites. Scientists need to query motif sequences with secondary structure assignments simultaneously. Scientist needs to search for the motif

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EFFICIENT KEYWORD SEARCH IN RELATIONAL DATABASES

EFFICIENT KEYWORD SEARCH IN RELATIONAL DATABASES

database that needs to know about structured query languages and database schema. Mostly users are not know to those things, so searching knowledge from relational databases is difficult to them. Where a keyword query input is a simple search model that can be issued by writing a list of keywords values, keyword search that place provide a solution of the problem. Because a keyword input query can be interpreted variously, a large number of outputs are returned. And indexing helps to easily retrieved answers and with the help of indexing we measure the performance of the CPU, execution time and Disk memory consumed.
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Open Dialogue Management for Relational Databases

Open Dialogue Management for Relational Databases

This paper addresses a particular type of infor- mation-seeking dialogue in which the user’s goal is to select a tuple from a table. Tuples are identified by constraints, attribute-value pairs elicited from a user during the dialogue. A typical user, however, cannot supply all values with equal readiness. For example, attributes such as primary or foreign keys are irrelevant or unintelligible to users. This results in a vocabulary problem, a mismatch between sys- tem and user vocabulary (Furnas et al., 1987). Fur- thermore, tables differ in their relevance to users. Tables that contain little semantic information have less potential to address user goals. Dialogue sys- tems for relational databases often rely on manual pre-processing to select the attributes a typical user can most readily supply and identify the tables with the most relevance to basic user goals. An open dialogue system obviates this manual step by exploiting the database semantics.
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Fuzzy Object Oriented Database versus FRDB for Uncertainty Management

Fuzzy Object Oriented Database versus FRDB for Uncertainty Management

Object oriented databases are considered better than relational databases ,due to increasing demand of new approaches to deal with complex data, complex relationship exiting among such data and large data intensive applications. Object oriented databases are much suitable for modern database applications, like CAD/CAM (Computer Aided Design/Computer Aided Manufacturing), CASE(Computer Aided Software Engineering), GIS (Geographical Information Systems), Spatial Databases, Office Automation; Knowledge based Systems, Hardware and Software Design, Network Management, Multimedia databases, VLSI (Very Large Scale Integrated) Design. In these applications, several types of information inexactness exist. Such incomplete and ill-defined information has been accepted, represented and manipulated with a certainty measure of acceptance using fuzzy techniques. So FOOD deal with different fuzzy concepts, like =almost all, =majority, =approximately, which include uncertainty. Complex object Structures can be represented well without fragmentation of aggregate data and complex relationship among attributes. Fuzzy object oriented database shows lack of formal semantics and algebra for manipulation and representation of knowledge as well as the inexact information data/information. Fuzzy relational database don’t use the concept of reusability, but reusability of classes allows for faster development and easier maintenance of the database and its application in FOOD.
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Horizontal Aggregations for Mining Relational Databases

Horizontal Aggregations for Mining Relational Databases

Our main goal is to define a template to generate SQL code combining aggregation and transposition (pivoting). A second goal is to extend the SELECT statement with a clause that combines transposition with aggregation. A method, SPJ method, is used to evaluate horizontal aggregations which relies on relational operations. That is, select, project, join and aggregation queries. In order to evaluate this query the query optimizer takes three input parameters: (1) the input table F, (2) the list of grouping columns L 1 ;…. ;L m

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In Memory Data processing using Redis Database

In Memory Data processing using Redis Database

Numerously recent applications that rely on storing and processing large amount of information, wants high availability and scalability which added more difficulties to relational database. Therefore an increasing number of companies have followed different categories of NoSQL data stores or non-relational databases, generally termed as NoSQL databases. NoSQL[2] is non-relational data storage system which does not require a fixed table schema, to replicate and distribute (partition) data over many servers. Today, NoSQL is used by large number of companies named as Adobe, Digg, Facebook, Foursquare, Google, Mozilla, etc.
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Outcome Analysis of Relational and Graph Databases

Outcome Analysis of Relational and Graph Databases

________________________________________________________________________________________________________ Abstract—Getting together enormous measures of complex data like information and learning is exceptionally regular these days. This requires the need to represent, store and manupulate complex data. From most recent three decades, the relational databases are being utilized as a part of numerous associations of different natures, for example, Instruction, Wellbeing, Business and in numerous different applications. Conventional databases indicate colossal execution and are intended to deal with organized information with ACID (Atomicity, Consistency, Isolation, Durability) property to oversee information uprightness but relational databases can't process appropriately and oversee substantial measure of information proficiently. Presently a day's advancements are moving towards movement, UI, Web of things, Program based IDEs and so on. These advancements require constant reaction and vast information store. A conventional database framework recovers and oversees database in a forbidden shape, yet in current situation of conveyed huge scale database those databases does not perform well. To conquer the impediments of customary databases, and to cover the necessities of current applications has lead the improvement of new database advances, for example, graph databases. We are showing an orderly examination of relational and graph database models, for that we are utilizing MySQL and Neo4j. Here we will think about the reasonableness of two classes of databases that is Relational database and graph database for putting away and questioning datasets. We will report aftereffects of estimations of scalability, query performance, and ease of query expression utilizing synthetic datasets.
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The Effects of Normalisation of the Satisfaction of Novice End-User Querying Databases

The Effects of Normalisation of the Satisfaction of Novice End-User Querying Databases

Overall, the results suggest that end-user satisfaction is greatest when querying databases in INF, as this is the level with the least complex queries, i.e., atomic data, no repeating f[r]

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G-Brackets : A New Formalism for Encoding and Querying GIS Databases

G-Brackets : A New Formalism for Encoding and Querying GIS Databases

A BSTRACT : In this paper, we outline the bridges that connect the area of GIS knowledge representation and its querying languages. Our work is in fact grounded in a research framework which we had founded to hang out with a pretty appropriate GIS query language that is adapted enough to this kind of information systems having a very particular aspects. Such aspects are unfortunately not yet fully exploited by the existing solutions.

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An Efficient and Scalable RDF Indexing Strategy based on B Hashed Bitmap Algorithm using CUDA

An Efficient and Scalable RDF Indexing Strategy based on B Hashed Bitmap Algorithm using CUDA

STORING AND INDEXING UNDER RELATIONAL PERSPECTIVE The RDF data in relational databases are stored as triples of a RDF graph on a single table over a relational schema Subject, Predicate,[r]

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Fusion Architecture of Database for Large and Diverse Dataset

Fusion Architecture of Database for Large and Diverse Dataset

With the change in the face of data, database technologies are also emerging with new challenges. Various approaches are being proposed and implemented in traditional relational and non-relational databases to improve the performance of the database and to increase the throughput as well as to decrease the computation overhead. One such approach was proposed in 10 by A.E.Lofty et al. They proposed a middle layer solution to be implemented between the web application and the database. This middle layer consisted of the load balancer, server detector, scalability manager, Transaction manager and data migrator. The middle layer solution offers to provide ACID properties in the non-relational databases. Another approach was presented by B.E James and P.O. Asagba in 11 . They implemented a hybrid database system with one SQL database and MongoDB as a non-relational database. This approach was for big data management. This approach combines the advantages of both relational and non-relational databases and reduces the disadvantages of both. Grolinger, Wilson, Tiwari and capretz proposed a methodology for using NoSQL databases for cloud environments in 12 . They proposed a combined approach of NoSQL and NewSQL together for cloud handling.
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