Data Mining - Concepts and Techniques.

XML is quickly becoming the de facto standard for data exchange over the Internet. This is creating a new set of data management requirements involving XML, such as the need to store and query XML documents. Researchers have proposed using relational database systems to satisfy these requirements by devising ways to "shred" XML documents into relations, and translate XML queries into SQL queries over these relations. However, a key issue with such an approach, which has largely been ignored in the research literature, is how (and whether) the ordered XML data model can be efficiently supported by the unordered relational data model. This paper shows that XML's ordered data model can indeed be efficiently supported by a relational database system. This is accomplished by encoding order as a data value. We propose three order encoding methods that can be used to represent XML order in the relational data model, and also propose algorithms for translating ordered XPath expressions into SQL using these encoding methods. Finally, we report the results of an experimental study that investigates the performance of the proposed order encoding methods on a workload of ordered XML queries and updates.

http://db.ucsd.edu/static/cse232b-archive/papers/tatarinov02.pdf

Storing and querying ordered XML using a relational database system

This paper describes the overall design and architecture of the Timber XML database system currently being implemented at the University of Michigan. The system is based upon a bulk algebra for manipulating trees, and natively stores XML. New access methods have been developed to evaluate queries in the XML context, and new cost estimation and query optimization techniques have also been developed. We present performance numbers to support some of our design decisions. We believe that the key intellectual contribution of this system is a comprehensive set-at-a-time query processing ability in a native XML store, with all the standard components of relational query processing, including algebraic rewriting and a cost-based optimizer.

https://deepblue.lib.umich.edu/bitstream/handle/2027.42/42328/20110274.pdf;sequence=1

TIMBER: A native XML database

Queries on XML documents typically combine selections on element contents, and, via path expressions, the structural relationships between tagged elements. Structural joins are used to find all pairs of elements satisfying the primitive structural relationships specified in the query, namely, parent-child and ancestor-descendant relationships. Efficient support for structural joins is thus the key to efficient implementations of XML queries. Recently proposed node numbering schemes enable the capturing of the XML document structure using traditional indices (such as B+-trees or R-trees). This paper proposes efficient structural join algorithms in the presence of tag indices. We first concentrate on using B+- trees and show how to expedite a structural join by avoiding collections of elements that do not participate in the join. We then introduce an enhancement (based on sibling pointers) that further improves performance. Such sibling pointers are easily implemented and dynamically maintainable. We also present a structural join algorithm that utilizes R-trees. An extensive experimental comparison shows that the B+-tree structural joins are more robust. Furthermore, they provide drastic improvement gains over the current state of the art.

/pdf/efficient-structural-joins-on-indexed-xml-documents-ayukhe6yko.pdf

Efficient structural joins on indexed XML documents

Finding all the occurrences of a twig pattern specified by a selection predicate on multiple elements in an XML document is a core operation for efficient evaluation of XML queries. Holistic twig join algorithms were proposed recently as an optimal solution when the twig pattern only involves ancestor-descendant relationships. In this paper, we address the problem of efficient processing of holistic twig joins on all/partly indexed XML documents. In particular, we propose an algorithm that utilizes available indices on element sets. While it can be shown analytically that the proposed algorithm is as efficient as the existing state-of-the-art algorithms in terms of worst case I/O and CPU cost, experimental results on various datasets indicate that the proposed index-based algorithm performs significantly better than the existing ones, especially when binary structural joins in the twig pattern have varying join selectivities.

/pdf/holistic-twig-joins-on-indexed-xml-documents-1ad6t9v4n6.pdf

Holistic twig joins on indexed XML documents

Storing And Querying XML Data Using RDBMS.

Energy simulation tool is a tool to simulate energy use by a building prior to the erection of the building. Commonly it has a feature providing alternative designs that are better than the user's design. In this paper, we propose a novel method in searching alternative design that is by using classification method. The classifiers we use are Naive Bayes, Decision Tree, and k-Nearest Neighbor. Our experiments hows that Decision Tree has the fastest classification time followed by Naive Bayes and k-Nearest Neighbor. The differences between classification time of Decision Tree and Naive Bayes also between Naive Bayes and k-NN are about an order of magnitude. Based on Percision, Recall, F- measure, Accuracy, and AUC, the performance of Naive Bayes is the best. It outperforms Decision Tree and k-Nearest Neighbor on all parameters but precision. Energy simulation tool is a tool to simulate energy use by a building prior to the erection of the building. The output of such simulation is a value in kWh/m 2 called energy performance. The calculation of the building energy performance must be carried out by developers as part of requirements to get permit to build the building. The building can only be built if the energy performance is below the allowable standard. In order to get building energy performance below the standard, architects must revise the design several times. And in order to ease the design work of the architects, an energy simulation tool must have a feature that suggests a better alternative design. Since the alternative design search is actually a classification problem, hence in this paper we propose a novel method to search alternative design by using classification method. The classification methods used in here are Decision Tree, Naive Bayes, and k-Nearest Neighbor. We will then compare the performance of these three methods in searching alternative design in an energy simulation tools.

https://thesai.org/Downloads/Volume4No11/Paper_5-Performance_Comparison_between_Naïve_Bayes.pdf

Performance Comparison between Naïve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool

The smart home is an environment, where heterogeneous electronic devices and appliances are networked together to provide smart services in a ubiquitous manner to the individuals. organization and people are wide accepting and adopting the functionalities offered by the smart home applications. this can be because of the various advantages, in easing users’ everyday life and work, provided by the rising internet of Things (IoT) technologies and devices, equipped with sensors, cameras, or actuators, and able either to accumulate information from the environment or to perform proper tasks. the main features of smart homes embrace realtime monitoring, remote control, safety from intruders, gas/fire alarm, and so on. Since among smart homes, sensitive and personal data are managed, security and privacy solutions should be put in place, to protect users/businesses’ data against violation try still on guarantee the supply of reliable services. As IoT home devices become increasingly ubiquitous, study’s findings and recommendations contribute to the broader understanding of users’ evolving attitudes towards privacy in smart homes.

/pdf/internet-of-things-iot-of-smart-home-privacy-and-security-vbb7zcyzup.pdf

Internet of Things (IoT) of Smart Home: Privacy and Security

Degrading or declining network performance in an installed computer network system is the most undesirable condition. There are several factors contribute to the decline of network performance, which their indications can be observed from quality changes in Quality of Service (QoS) parameters measurement result. This research proposes recommendations in improving network performance towards the changes of QoS parameters quality.

https://www.ijcncs.org/published/volume3/issue2/p5_3-2.pdf

THE IMPACT OF QoS CHANGES TOWARDS NETWORK PERFORMANCE

Internet crimes are now increasing. In a row with many crimes using information technology, in particular those using Internet, some crimes are often carried out in the form of attacks that occur within a particular agency or institution. To be able to find and identify the types of attacks, requires a long process that requires time, human resources and utilization of information technology to solve these problems. The process of identifying attacks that happened also needs the support of both hardware and software as well. The attack happened in the Internet network can generally be stored in a log file that has a specific data format. Clustering technique is one of methods that can be used to facilitate the identification process. Having grouped the data log file using K-means clustering technique, then the data is grouped into three categories of attack, and will be continued with the forensic process that can later be known to the source and target of attacks that exist in the network. It is concluded that the framework proposed can help the investigator in the trial process.

Ahmad Ashari

Papers

Storing And Querying XML Data Using RDBMS.

Performance Comparison between Naïve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool

Internet of Things (IoT) of Smart Home: Privacy and Security

THE IMPACT OF QoS CHANGES TOWARDS NETWORK PERFORMANCE

Log Analysis Techniques using Clustering in Network Forensics.