Search results for: Spatiotemporal databases.

Authors: M. R. Abbasifard, M. Rahgozar, A. Bayati, P. Pournemati

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One important problem in today organizations is the existence of non-integrated information systems, inconsistency and lack of suitable correlations between legacy and modern systems. One main solution is to transfer the local databases into a global one. In this regards we need to extract the data structures from the legacy systems and integrate them with the new technology systems. In legacy systems, huge amounts of a data are stored in legacy databases. They require particular attention since they need more efforts to be normalized, reformatted and moved to the modern database environments. Designing the new integrated (global) database architecture and applying the reverse engineering requires data normalization. This paper proposes the use of database reverse engineering in order to integrate legacy and modern databases in organizations. The suggested approach consists of methods and techniques for generating data transformation rules needed for the data structure normalization.

Keywords: Reverse Engineering, Database Integration, System Integration, Data Structure Normalization

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Authors: N. D. Karande, G. A. Patil

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Databases have become ubiquitous. Almost all IT applications are storing into and retrieving information from databases. Retrieving information from the database requires knowledge of technical languages such as Structured Query Language (SQL). However majority of the users who interact with the databases do not have a technical background and are intimidated by the idea of using languages such as SQL. This has led to the development of a few Natural Language Database Interfaces (NLDBIs). A NLDBI allows the user to query the database in a natural language. This paper highlights on architecture of new NLDBI system, its implementation and discusses on results obtained. In most of the typical NLDBI systems the natural language statement is converted into an internal representation based on the syntactic and semantic knowledge of the natural language. This representation is then converted into queries using a representation converter. A natural language query is translated to an equivalent SQL query after processing through various stages. The work has been experimented on primitive database queries with certain constraints.

Keywords: Natural language database interface, representation converter, syntactic and semantic knowledge

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Authors: Hendry Muljadi, Jiro Araki, Satoru Miyazaki, Asao Fujiyama

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There are so many databases of various fields of life sciences available online. To find well-used databases, a survey to measure life science database citation frequency in scientific literatures is done. The survey is done by measuring how many scientific literatures which are available on PubMed Central archive cited a specific life science database. This paper presents and discusses the results of the survey.

Keywords: Life science, database, metadatabase, PubMedCentral.

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Authors: Justus S, K Iyakutti

Abstract:

Object Relational Databases (ORDB) are complex in nature than traditional relational databases because they combine the characteristics of both object oriented concepts and relational features of conventional databases. Design of an ORDB demands efficient and quality schema considering the structural, functional and componential traits. This internal quality of the schema is assured by metrics that measure the relevant attributes. This is extended to substantiate the understandability, usability and reliability of the schema, thus assuring external quality of the schema. This work institutes a formalization of ORDB metrics; metric definition, evaluation methodology and the calibration of the metric. Three ORDB schemas were used to conduct the evaluation and the formalization of the metrics. The metrics are calibrated using content and criteria related validity based on the measurability, consistency and reliability of the metrics. Nominal and summative scales are derived based on the evaluated metric values and are standardized. Future works pertaining to ORDB metrics forms the concluding note.

Keywords: Measurements, Product metrics, Metrics calibration, Object-relational database.

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Authors: Rima Hleiss, Elie Bitar, Mahmoud Hassan, Mohamad Khalil

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A comprehensive study of object recognition in the human brain requires combining both spatial and temporal analysis of brain activity. Here, we are mainly interested in three issues: the time perception of visual objects, the ability of discrimination between two particular categories (objects vs. animals), and the possibility to identify a particular spatial representation of visual objects. Our experiment consisted of acquiring dense electroencephalographic (EEG) signals during a picture-naming task comprising a set of objects and animals’ images. These EEG responses were recorded from nine participants. In order to determine the time perception of the presented visual stimulus, we analyzed the Event Related Potentials (ERPs) derived from the recorded EEG signals. The analysis of these signals showed that the brain perceives animals and objects with different time instants. Concerning the discrimination of the two categories, the support vector machine (SVM) was applied on the instantaneous EEG (excellent temporal resolution: on the order of millisecond) to categorize the visual stimuli into two different classes. The spatial differences between the evoked responses of the two categories were also investigated. The results showed a variation of the neural activity with the properties of the visual input. Results showed also the existence of a spatial pattern of electrodes over particular regions of the scalp in correspondence to their responses to the visual inputs.

Keywords: Brain activity, dense EEG, evoked responses, spatiotemporal analysis, SVM, perception.

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Authors: A.M. Fahim, A. M. Salem, F. A. Torkey, M. A. Ramadan

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Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, a density based clustering algorithm (DCBRD) is presented, relying on a knowledge acquired from the data by dividing the data space into overlapped regions. The proposed algorithm discovers arbitrary shaped clusters, requires no input parameters and uses the same definitions of DBSCAN algorithm. We performed an experimental evaluation of the effectiveness and efficiency of it, and compared this results with that of DBSCAN. The results of our experiments demonstrate that the proposed algorithm is significantly efficient in discovering clusters of arbitrary shape and size.

Keywords: Clustering Algorithms, Arbitrary Shape of clusters, cluster Analysis.

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Authors: Petr Morávek, Miloš Šeda

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In practice, we often come across situations where it is necessary to make decisions based on incomplete or uncertain data. In control systems it may be due to the unknown exact mathematical model, or its excessive complexity (e.g. nonlinearity) when it is necessary to simplify it, respectively, to solve it using a rule base. In the case of databases, searching data we compare a similarity measure with of the requirements of the selection with stored data, where both the select query and the data itself may contain vague terms, for example in the form of linguistic qualifiers. In this paper, we focus on the processing of uncertain data in databases and demonstrate it on the example multi-criteria decision making in the selection of variants, specified by higher number of technical parameters.

Keywords: fuzzy logic, linguistic variable, multicriteria decision

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Authors: Su Cheng Haw, G. S. V. Radha Krishna Rao

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Over the past few years, XML (eXtensible Mark-up Language) has emerged as the standard for information representation and data exchange over the Internet. This paper provides a kick-start for new researches venturing in XML databases field. We survey the storage representation for XML document, review the XML query processing and optimization techniques with respect to the particular storage instance. Various optimization technologies have been developed to solve the query retrieval and updating problems. Towards the later year, most researchers proposed hybrid optimization techniques. Hybrid system opens the possibility of covering each technology-s weakness by its strengths. This paper reviews the advantages and limitations of optimization techniques.

Keywords: indexing, labeling scheme, query optimization, XML storage.

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Authors: Nadeem Iftikhar, Zouhaib Zafar, Shaukat Ali

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Efficient retrieval of multimedia objects has gained enormous focus in recent years. A number of techniques have been suggested for retrieval of textual information; however, relatively little has been suggested for efficient retrieval of multimedia objects. In this paper we have proposed a generic architecture for contextaware retrieval of multimedia objects. The proposed framework combines the well-known approaches of text-based retrieval and context-aware retrieval to formulate architecture for accurate retrieval of multimedia data.

Keywords: Context-aware retrieval, information retrieval, multimedia databases, multimedia data.

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Authors: F. Raja, M.Rahgozar, N. Razavi, M. Siadaty

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Main Memory Database systems (MMDB) store their data in main physical memory and provide very high-speed access. Conventional database systems are optimized for the particular characteristics of disk storage mechanisms. Memory resident systems, on the other hand, use different optimizations to structure and organize data, as well as to make it reliable. This paper provides a brief overview on MMDBs and one of the memory resident systems named FastDB and compares the processing time of this system with a typical disc resident database based on the results of the implementation of TPC benchmarks environment on both.

Keywords: Disk-Resident Database, FastDB, Main MemoryDatabase.

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Authors: Huda Al Shuaily, Karen Renaud

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Databases comprise the foundation of most software systems. System developers inevitably write code to query these databases. The de facto language for querying is SQL and this, consequently, is the default language taught by higher education institutions. There is evidence that learners find it hard to master SQL, harder than mastering other programming languages such as Java. Educators do not agree about explanations for this seeming anomaly. Further investigation may well reveal the reasons. In this paper, we report on our investigations into how novices learn SQL, the actual problems they experience when writing SQL, as well as the differences between expert and novice SQL query writers. We conclude by presenting a model of SQL learning that should inform the instructional material design process better to support the SQL learning process.

Keywords: Pattern, SQL, learning, model.

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Authors: Harleen Kaur

Abstract:

Knowledge Discovery in Databases (KDD) is the process of extracting previously unknown, hidden and interesting patterns from a huge amount of data stored in databases. Data mining is a stage of the KDD process that aims at selecting and applying a particular data mining algorithm to extract an interesting and useful knowledge. It is highly expected that data mining methods will find interesting patterns according to some measures, from databases. It is of vital importance to define good measures of interestingness that would allow the system to discover only the useful patterns. Measures of interestingness are divided into objective and subjective measures. Objective measures are those that depend only on the structure of a pattern and which can be quantified by using statistical methods. While, subjective measures depend only on the subjectivity and understandability of the user who examine the patterns. These subjective measures are further divided into actionable, unexpected and novel. The key issues that faces data mining community is how to make actions on the basis of discovered knowledge. For a pattern to be actionable, the user subjectivity is captured by providing his/her background knowledge about domain. Here, we consider the actionability of the discovered knowledge as a measure of interestingness and raise important issues which need to be addressed to discover actionable knowledge.

Keywords: Data Mining Community, Knowledge Discovery inDatabases (KDD), Interestingness, Subjective Measures, Actionability.

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Authors: Hassan M. Najadat

Abstract:

A new approach for timestamp ordering problem in serializable schedules is presented. Since the number of users using databases is increasing rapidly, the accuracy and needing high throughput are main topics in database area. Strict 2PL does not allow all possible serializable schedules and so does not result high throughput. The main advantages of the approach are the ability to enforce the execution of transaction to be recoverable and the high achievable performance of concurrent execution in central databases. Comparing to Strict 2PL, the general structure of the algorithm is simple, free deadlock, and allows executing all possible serializable schedules which results high throughput. Various examples which include different orders of database operations are discussed.

Keywords: Concurrency control, schedule, timestamp, transaction.

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Authors: Pavol Tanuska, Pavel Vazan, Michal Kebisek, Dominika Jurovata

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The paper gives the results of the project that was oriented on the usage of knowledge discoveries from production systems for needs of the hierarchical process control. One of the main project goals was the proposal of knowledge discovery model for process control. Specifics data mining methods and techniques was used for defined problems of the process control. The gained knowledge was used on the real production system thus the proposed solution has been verified. The paper documents how is possible to apply the new discovery knowledge to use in the real hierarchical process control. There are specified the opportunities for application of the proposed knowledge discovery model for hierarchical process control.

Keywords: Hierarchical process control, knowledge discovery from databases, neural network.

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Authors: Aitana Alonso-Nogueira, Helia Estévez-Fernández, Isaías García

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This paper presents an approach to reduce some of its current flaws in the requirements phase inside the software development process. It takes the software requirements of an application, makes a conceptual modeling about it and formalizes it within JSON documents. This formal model is lodged in a NoSQL database which is document-oriented, that is, MongoDB, because of its advantages in flexibility and efficiency. In addition, this paper underlines the contributions of the detailed approach and shows some applications and benefits for the future work in the field of automatic code generation using model-driven engineering tools.

Keywords: Conceptual modeling, JSON, NoSQL databases, requirements engineering, software development.

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Authors: M. Jamali, F. Taghiyareh

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The problem of frequent itemset mining is considered in this paper. One new technique proposed to generate frequent patterns in large databases without time-consuming candidate generation. This technique is based on focusing on transaction instead of concentrating on itemset. This algorithm based on take intersection between one transaction and others transaction and the maximum shared items between transactions computed instead of creating itemset and computing their frequency. With applying real life transactions and some consumption is taken from real life data, the significant efficiency acquire from databases in generation association rules mining.

Keywords: Association rules, data mining, frequent patterns, shared itemset.

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Authors: Sandeep Chandana, Rene V. Mayorga, Christine W. Chan

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This article outlines conceptualization and implementation of an intelligent system capable of extracting knowledge from databases. Use of hybridized features of both the Rough and Fuzzy Set theory render the developed system flexibility in dealing with discreet as well as continuous datasets. A raw data set provided to the system, is initially transformed in a computer legible format followed by pruning of the data set. The refined data set is then processed through various Rough Set operators which enable discovery of parameter relationships and interdependencies. The discovered knowledge is automatically transformed into a rule base expressed in Fuzzy terms. Two exemplary cancer repository datasets (for Breast and Lung Cancer) have been used to test and implement the proposed framework.

Keywords: Knowledge Extraction, Fuzzy Sets, Rough Sets, Neuro–Fuzzy Systems, Databases

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Authors: Jinrui Ren, Li Wu

Abstract:

Anthropogenic activities and changes of underlying surface affect the temporal and spatial distribution of surface temperature in Kunming. Taking Kunming city as the research area, the surface temperature in 2000, 2010 and 2020 as the research object, using ENVI 5.3 and ArcGIS 10.8 as auxiliary tools, and based on the spatial autocorrelation method, this paper devoted to exploring the interactions among the changes of surface temperature, urban heat island effect and land use type, so as to provide theoretical basis and scientific basis for mitigating climate change. The results showed that: (1) The heat island effect was obvious in Kunming City, the high temperature area increased from 604 km2 in 2000 to 1269 km2 in 2020, and the sub-high temperature area reached 1099 km2 in 2020; (2) In terms of space, the spatial distribution of LST was significantly different with the change of underlying surface. The high temperature zone extended in three directions: south, north and east. The overall spatial distribution pattern of LST was high in the east and low in the west. (3) The inter-annual fluctuation of land surface temperature (LST) was large, and the growth rate was faster, from 2000 to 2010. The lowest temperature in 2000 was 13.45 ℃, which raised to 19.71 ℃ in 2010, and the temperature difference in 10 years was 6.26 ℃. (4) The land use/land cover type has a strong effect on the change of LST: the man-made land made a great contribution to the increase of LST, followed by grassland and farmland, while forest and water have a significant cooling effect on LST. To sum up, the variation of surface temperature in Kunming is the result of the interactions of human activities and climate change.

Keywords: Surface temperature, urban heat island effect, land use cover type, spatiotemporal variation.

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Authors: Eiad Yafi, M. A. Alam, Ranjit Biswas

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Knowledge Discovery of Databases (KDD) is the process of extracting previously unknown but useful and significant information from large massive volume of databases. Data Mining is a stage in the entire process of KDD which applies an algorithm to extract interesting patterns. Usually, such algorithms generate huge volume of patterns. These patterns have to be evaluated by using interestingness measures to reflect the user requirements. Interestingness is defined in different ways, (i) Objective measures (ii) Subjective measures. Objective measures such as support and confidence extract meaningful patterns based on the structure of the patterns, while subjective measures such as unexpectedness and novelty reflect the user perspective. In this report, we try to brief the more widely spread and successful subjective measures and propose a new subjective measure of interestingness, i.e. shocking.

Keywords: Shocking rules (SHR).

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Authors: Humaira Liaquat, Nadeem Iftikhar, Shaukat Ali, Zohaib Zafar Iqbal

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In the current age, retrieval of relevant information from massive amount of data is a challenging job. Over the years, precise and relevant retrieval of information has attained high significance. There is a growing need in the market to build systems, which can retrieve multimedia information that precisely meets the user's current needs. In this paper, we have introduced a framework for refining query results before showing it to the user, using ambient intelligence, user profile, group profile, user location, time, day, user device type and extracted features. A prototypic tool was also developed to demonstrate the efficiency of the proposed approach.

Keywords: Context aware retrieval, Information retrieval, Ambient Intelligence, Multimedia databases, User and group profile.

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Authors: Cherif Haddad, Faouzi Ben Charrada

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On a such wide-area environment as a Grid, data placement is an important aspect of distributed database systems. In this paper, we address the problem of initial placement of database no-replicated fragments in Grid architecture. We propose a graph based approach that considers resource restrictions. The goal is to optimize the use of computing, storage and communication resources. The proposed approach is developed in two phases: in the first phase, we perform fragment grouping using knowledge about fragments dependency and, in the second phase, we determine an efficient placement of the fragment groups on the Grid. We also show, via experimental analysis that our approach gives solutions that are close to being optimal for different databases and Grid configurations.

Keywords: Grid computing, Distributed systems, Data resourcesmanagement, Database systems, Database placement.

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Authors: Tibor Kulcsar, Gabor Sarossy, Gabor Bereznai, Robert Auer, Janos Abonyi

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On-line (near infrared) spectroscopy is widely used to support the operation of complex process systems. Information extracted from spectral database can be used to estimate unmeasured product properties and monitor the operation of the process. These techniques are based on looking for similar spectra by nearest neighborhood algorithms and distance based searching methods. Search for nearest neighbors in the spectral space is an NP-hard problem, the computational complexity increases by the number of points in the discrete spectrum and the number of samples in the database. To reduce the calculation time some kind of indexing could be used. The main idea presented in this paper is to combine indexing and visualization techniques to reduce the computational requirement of estimation algorithms by providing a two dimensional indexing that can also be used to visualize the structure of the spectral database. This 2D visualization of spectral database does not only support application of distance and similarity based techniques but enables the utilization of advanced clustering and prediction algorithms based on the Delaunay tessellation of the mapped spectral space. This means the prediction has not to use the high dimension space but can be based on the mapped space too. The results illustrate that the proposed method is able to segment (cluster) spectral databases and detect outliers that are not suitable for instance based learning algorithms.

Keywords: indexing high dimensional databases, dimensional reduction, clustering, similarity, k-nn algorithm.

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Authors: Mounira Taileb, Sami Touati

Abstract:

In Content-Based Image Retrieval systems it is important to use an efficient indexing technique in order to perform and accelerate the search in huge databases. The used indexing technique should also support the high dimensions of image features. In this paper we present the hierarchical index NOHIS-tree (Non Overlapping Hierarchical Index Structure) when we scale up to very large databases. We also present a study of the influence of clustering on search time. The performance test results show that NOHIS-tree performs better than SR-tree. Tests also show that NOHIS-tree keeps its performances in high dimensional spaces. We include the performance test that try to determine the number of clusters in NOHIS-tree to have the best search time.

Keywords: High-dimensional indexing, k-nearest neighborssearch.

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Authors: Alae El Alami, Mohamed Bahaj

Abstract:

This paper proposes an approach for translating an existing relational database (RDB) schema into ORDB. The transition is done with methods that can extract various functions from a RDB which is based on aggregations, associations between the various tables, and the reflexive relationships. These methods can extract even the inheritance knowing that no process of reverse engineering can know that it is an Inheritance; therefore, our approach exceeded all of the previous studies made for ​​the transition from RDB to ORDB. In summation, the creation of the New Data Model (NDM) that stocks the RDB in a form of a structured table, and from the NDM we create our navigational model in order to simplify the implementation object from which we develop our different types. Through these types we precede to the last step, the creation of tables.

The step mentioned above does not require any human interference. All this is done automatically, and a prototype has already been created which proves the effectiveness of this approach.

Keywords: Relational databases, Object-relational databases, Semantic enrichment.

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Authors: Miriam Sosa-Díaz, Faustino Sánchez-Garduño

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In population dynamics the study of both, the abundance and the spatial distribution of the populations in a given habitat, is a fundamental issue a From ecological point of view, the determination of the factors influencing such changes involves important problems. In this paper a mathematical model to describe the temporal dynamic and the spatiotemporal dynamic of the interaction of three populations (pollinators, plants and herbivores) is presented. The study we present is carried out by stages: 1. The temporal dynamics and 2. The spatio-temporal dynamics. In turn, each of these stages is developed by considering three cases which correspond to the dynamics of each type of interaction. For instance, for stage 1, we consider three ODE nonlinear systems describing the pollinator-plant, plant-herbivore and plant-pollinator-herbivore, interactions, respectively. In each of these systems different types of dynamical behaviors are reported. Namely, transcritical and pitchfork bifurcations, existence of a limit cycle, existence of a heteroclinic orbit, etc. For the spatiotemporal dynamics of the two mathematical models a novel factor are introduced. This consists in considering that both, the pollinators and the herbivores, move towards those places of the habitat where the plant population density is high. In mathematical terms, this means that the diffusive part of the pollinators and herbivores equations depend on the plant population density. The analysis of this part is presented by considering pairs of populations, i. e., the pollinator-plant and plant-herbivore interactions and at the end the two mathematical model is presented, these models consist of two coupled nonlinear partial differential equations of reaction-diffusion type. These are defined on a rectangular domain with the homogeneous Neumann boundary conditions. We focused in the role played by the density dependent diffusion term into the coexistence of the populations. For both, the temporal and spatio-temporal dynamics, a several of numerical simulations are included.

Keywords: Bifurcation, heteroclinic orbits, steady state, traveling wave.

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Authors: Sitansu Kumar Verma, Soni Yadav, Jitendra Singh, Shraddha, Ajay Kumar

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MicroRNAs (miRNAs), a class of approximately 22 nucleotide long non coding RNAs which play critical role in different biological processes. The mature microRNA is usually 19–27 nucleotides long and is derived from a bigger precursor that folds into a flawed stem-loop structure. Mature micro RNAs are involved in many cellular processes that encompass development, proliferation, stress response, apoptosis, and fat metabolism by gene regulation. Resent finding reveals that certain viruses encode their own miRNA that processed by cellular RNAi machinery. In recent research indicate that cellular microRNA can target the genetic material of invading viruses. Cellular microRNA can be used in the virus life cycle; either to up regulate or down regulate viral gene expression Computational tools use in miRNA target prediction has been changing drastically in recent years. Many of the methods have been made available on the web and can be used by experimental researcher and scientist without expert knowledge of bioinformatics. With the development and ease of use of genomic technologies and computational tools in the field of microRNA biology has superior tremendously over the previous decade. This review attempts to give an overview over the genome wide approaches that have allow for the discovery of new miRNAs and development of new miRNA target prediction tools and databases.

Keywords: MicroRNAs, computational tools, gene regulation, databases, RNAi.

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Authors: Pavel Y. Tabakov, Kevin Duffy

Abstract:

The present study presents a new approach to automatic data clustering and classification problems in large and complex databases and, at the same time, derives specific types of explicit rules describing each cluster. The method works well in both sparse and dense multidimensional data spaces. The members of the data space can be of the same nature or represent different classes. A number of N-dimensional ellipsoids are used for enclosing the data clouds. Due to the geometry of an ellipsoid and its free rotation in space the detection of clusters becomes very efficient. The method is based on genetic algorithms that are used for the optimization of location, orientation and geometric characteristics of the hyper-ellipsoids. The proposed approach can serve as a basis for the development of general knowledge systems for discovering hidden knowledge and unexpected patterns and rules in various large databases.

Keywords: Classification, clustering, data minig, genetic algorithms.

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Authors: Khalid A. Kaabneh

Abstract:

This paper introduces and studies new indexing techniques for content-based queries in images databases. Indexing is the key to providing sophisticated, accurate and fast searches for queries in image data. This research describes a new indexing approach, which depends on linear modeling of signals, using bases for modeling. A basis is a set of chosen images, and modeling an image is a least-squares approximation of the image as a linear combination of the basis images. The coefficients of the basis images are taken together to serve as index for that image. The paper describes the implementation of the indexing scheme, and presents the findings of our extensive evaluation that was conducted to optimize (1) the choice of the basis matrix (B), and (2) the size of the index A (N). Furthermore, we compare the performance of our indexing scheme with other schemes. Our results show that our scheme has significantly higher performance.

Keywords: Axial Projection, images, indexing, multimedia database, searching.

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Authors: Cherif Haddad, Faouzi Ben Charrada

Abstract:

Large-scale systems such as Grids offer infrastructures for both data distribution and parallel processing. The use of Grid infrastructures is a more recent issue that is already impacting the Distributed Database Management System industry. In DBMS, distributed query processing has emerged as a fundamental technique for ensuring high performance in distributed databases. Database placement is particularly important in large-scale systems because it reduces communication costs and improves resource usage. In this paper, we propose a dynamic database placement policy that depends on query patterns and Grid sites capabilities. We evaluate the performance of the proposed database placement policy using simulations. The obtained results show that dynamic database placement can significantly improve the performance of distributed query processing.

Keywords: Large-scale systems, Grid environment, Distributed Databases, Distributed query processing, Database placement

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Authors: Vasudha Bhatnagar, Ahmed Sultan Al-Hegami, Naveen Kumar

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Rule Discovery is an important technique for mining knowledge from large databases. Use of objective measures for discovering interesting rules leads to another data mining problem, although of reduced complexity. Data mining researchers have studied subjective measures of interestingness to reduce the volume of discovered rules to ultimately improve the overall efficiency of KDD process. In this paper we study novelty of the discovered rules as a subjective measure of interestingness. We propose a hybrid approach based on both objective and subjective measures to quantify novelty of the discovered rules in terms of their deviations from the known rules (knowledge). We analyze the types of deviation that can arise between two rules and categorize the discovered rules according to the user specified threshold. We implement the proposed framework and experiment with some public datasets. The experimental results are promising.

Keywords: Knowledge Discovery in Databases (KDD), Interestingness, Subjective Measures, Novelty Index.

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