A new rational algorithm for view updating in relational databases

Delhibabu, Radhakrishnan; Behrend, Andreas

doi:10.1007/s10489-014-0579-0

Cited by 6 publications

(3 citation statements)

References 43 publications

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“…We begin by recalling previous work on view deletion. Chandrabose [6,7] and Delhibabu [34,35,36], defines a contraction and revision operator in view deletion with respect to a set of formulae or sentences using Hansson's [54] belief change. Similar to our approach, he focused on set of formulae or sentences in knowledge base revision for view update wrt.…”

Section: Related Workmentioning

confidence: 99%

Comparative study of view update algorithms in rational choice theory

Delhibabu¹

2014

Appl Intell

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The dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. We show that knowledge base dynamics has interesting connection with kernel change via hitting set and abduction. The approach extends and integrates standard techniques for efficient query answering and integrity checking. The generation of hitting set is carried out through a hyper tableaux calculus and magic set that is focused on the goal of minimality. Many different view update algorithms have been proposed in the literature to address this problem. The present paper provides a comparative study of view update algorithms in rational approach. ⋆ This paper extends work from Delhibabu [34] and Mayol [83] Our knowledge base dynamics is defined in two parts: an immutable part (formulae) and updatable part (literals) (for definition and properties see works of Nebel [88] and Segerberg [98]). Knowledge bases have a set of integrity constraints (see the definitions in later section). In the case of finite knowledge bases, it is sometimes hard to see how the update relations should be modified to accomplish certain knowledge base updates.Example 1. Consider a database with an (immutable) rule that a staff member is a person who is currently working in a research group under a chair. Additional (updatable) facts are that matthias and gerhard are group chairs, and delhibabu and aravindan are staff members in group info1. Our first integrity constraint (IC) is that each research group has only one chair ie. ∀x, y, z (y=x) ← group chair(x,y) ∧ group chair(x,z). Second integrity constraint is that a person can be a chair for only one research group ie. ∀x, y, z (y=z)← group chair(y,x) ∧ group chair(z,x).Immutable part: staff chair(X,Y)← staff group(X,Z),group chair(Z,Y). Updatable part: group chair(infor1,matthias)← group chair(infor2,gerhard)← staff group(delhibabu,infor1)← staff group(aravindan,infor1)← Suppose we want to update this database with the information, staff chair(aravindan,gerhard); From the immutable part, we can deduce that this can be achieved by asserting staff group(aravindan,Z) group chair(Z,gerhard)If we are restricted to definite clauses, there are three plausible ways to do this: first case is, aravindan and gerhard belong to infor1, i.e, staff group(aravindan,-info1) group chair(info1,gerhard). We need to delete both base facts group chair-(infor1,matthias)← and group chair(infor2,gerhard)←, because our first IC as well as second IC would be violated otherwise. In order to change the view, we need to insert group chair(infor1,gerhard)← as a base fact. Assume that we have an algorithm that deletes the base facts staff group(delhibabu,infor1)← from the database. But, no rational person will agree with such an algorithm, because the fact staff group(delhibabu,infor1)← is not "relevant" to the view atom.Second case, aravindan and gerhard belong to infor2, that is staff group(aravindan,info2) group chair(info2,gerhard). Simply, insert the new fact ...

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Section: Related Workmentioning

confidence: 99%

Comparative study of view update algorithms in rational choice theory

Delhibabu¹

2014

Appl Intell

Self Cite

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“…Massive knowledge representation and management is emerged as a challengeable issue in big data era. Managing and storing knowledge in traditional databases [24]- [26] could no longer fulfill the increasing requirements of dealing with massive data. There are number of projects (e.g., [27], [28]) have been developed as an alternative to traditional database systems.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Modeling of Object-Oriented Biomedical Information in HBase

Zhang

Sun

et al. 2020

IEEE Access

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While object-oriented databases (OODBs) are known to be rich in functionality, HBase database, which is a distributed and scalable big data store, as well as uncertain databases have recently gained a lot of attention in the database community. This paper presents a methodology for handling an important step of knowledge integrations and migrations. In particular, a formal approach for reengineering fuzzy object-oriented databases in HBase is firstly developed. The reengineering approach is based on the technique of rule-based schema mapping, which defines a set of transformation rules involved in the process of schema transformations for mapping a fuzzy object-oriented database schema into a fuzzy HBase database schema. In addition, a formal approach to map the fuzzy object-oriented algebra into fuzzy HBase algebra is proposed. On this basis, we complement the work with a comprehensive set of experiments to show the efficiency of our proposed approach in terms of query time and scalability metrics. INDEX TERMS Big data store, fuzzy HBase, fuzzy object-oriented database, mapping.

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“…In the mining of data, the optimized space-time indexing algorithm is usually used to make the mining effect better. The algorithm uses the multidimensional indexing technique to transform the index to the spatiotemporal dimension, and realize the establishment and improvement of the space-time index [9].…”

Section: Introductionmentioning

confidence: 99%

Data Mining Technology Based on Association Rules Algorithm

2019

IJOMAM

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The current data mining technology has the mismatch problem of mining keywords and used words, which leads to low mining precision and low efficiency. In this paper, a new data mining algorithm based on association rule algorithm is proposed. Firstly, the weight of the keywords in the data mining process is calculated by using the TF-IDF function. Based on the analysis of the association rules algorithm and the vector space model, the information retrieval model is constructed. On the basis of this, the association rule algorithm including the initial mining items is used to establish the rule database, and several words with the highest degree of relevance to the mining words are selected as the extension words, which is combined with the initial mining into a new mining. Finally, the clustering analysis of new mining results are carried out by K-means clustering algorithm, and according to the descending order, the relevance is ordered, to output mining results. The experimental results show that the algorithm is accurate and efficient.

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A new rational algorithm for view updating in relational databases

Cited by 6 publications

References 43 publications

Comparative study of view update algorithms in rational choice theory

Comparative study of view update algorithms in rational choice theory

Uncertainty Modeling of Object-Oriented Biomedical Information in HBase

Data Mining Technology Based on Association Rules Algorithm

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