A Framework for Mining and Querying Summarized XML Data through Tree-Based Association Rules

XML actually developed as a benchmark for caching, dispense and interchanging data over multiple platforms. The XML data is on the grow over the time in fast rate. Enterprises want formulating queries on XML datasets habitually. As giant XML data is retrievable, it is not easy job to pull out vital data from XML dataset. It is computationally expensive to answer queries without any sustain. Towards this, in this paper we introduce a method known as Tree-based Association Rules (TARs) mined policy that make available necessary info on structure and content of XML file and the TARs are also placed in XML design. The extracted data (TARs) used in future on for XML query answering support system. This technique gives rapid and precise answer. We have also introduced a model to show the usefulness of the designed structure. The experimental outcomes are very accommodating and query answering is likely to be important in real time uses.

A database management system (DBMS) should provide capabilities for defining and enforcing certain integrity constraints that must hold for the data recorded in the database. These constraints are derived from the semantics of the data and from the rules of the system under study (SuS) that database represents. Every DBMS is based on a data model that provides the means to achieve data abstraction and to express database schema. Each data model has a specific set of integrity constraints types. Some of constraint types are common for several data models and some of them are typical for a data model. Beyond well-known constraint types there are more complex ones that are difficult to express and enforce within the data model. In this paper we deal with the extended tuple constraint (ETC) type. It is the extension of tuple constraint that is one of constraint types of relational data model. In this paper we explain the usage of extended tuple constraint in relational data model and its implementation in relational DBMSs. XML data model does not recognize ETC type. We specify the extended tuple constraint in XML data model and propose two techniques for implementation of ETC in XML DBMSs. Each of them depends on the characteristics of a selected XML DBMS. We use XQuery functions in eXist DBMS, and triggers in Sedna DBMS.

Mining XML association rule is confronted with more challenges due to the inherent flexibilities of XML in both structure and semantics. In order to make mining XML association rule efficiently, we give a new definition of transaction and item in XML context, then build transaction database based on an index table. Based on our definition and the index table used for XML searching, we can check the include relation between a transaction and an item quickly. A high adaptive mining technique is also described. By using it, we can process mining rules with no guidance of interest associations given by users and mining unknown rules. We demonstrate the effectiveness of these techniques through experiments on real-life data.

As XML data storage and interchange become ubiquitous, analysts and data engineers increasingly need tools to model their data and map it to XML schemas and to reverse engineer XML documents and schemas in support of evolution and integration activities. For effective data management, model transformations require guarantees of properties of interest including guarantees of information and constraintpreservation, redundancy-free and compactness guarantees, and assurances about readability and maintainability. In this paper, we make foundational observations about XML data management, including conceptual modeling for XML data, transformations to and from XML Schema and XML data models, and transformation guarantees concerning properties of interest, and we provide resolutions for conceptual mismatches between XML data management and more traditional data management. Our implemented prototype tools show that these observations and insights can provide a strong foundation for XML data engineering.

Because of increasing use of XML data on the internet, the need for an efficient method of storing and querying XML data is vital. So far, two major types of system for XML data management have been introduced: XML Enabled systems and XML native systems. The former uses relational database system for storing and querying XML data and the latter is a special XML database system which is based on XML data model. Since relational database systems are more mature than XML native systems, it seems that the use of abilities and efficiencies of relational systems is more economical. In this article, we have introduced a new relational schema named XLight for storing and processing XML data, and we have compared it with some similar methods.

Efficiently mining frequent itemsets and association rules on the encrypted outsourced data remains a great challenge for the time-consuming ciphertext computations. Nowadays, it has been not well addressed for privacy-preserving frequent itemsets and association rule mining schemes with mining efficiency, dataset, and query confidentiality simultaneously. In this paper, we investigate the study of privacy issues on frequent itemset mining and association rule mining on outsourced data in a two-cloud model, where the data are encrypted and outsourced by multiple owners holding different public keys. We develop several secure computation protocols based on additively homomorphic cryptosystem and additive secret sharing, which enable the clouds could securely mine the frequent itemsets and association rules. Furthermore, we also design two kinds of frequent itemset and association rule query service models, i.e., service customers query the cloud-mined results, and service customers query with their own decided threshold. The proposed scheme not only supports the mining process on the data encrypted by multiple public keys without compromising the security of the datasets, query data and query results, but also offline users. In addition, the experimental results show that our query scheme is much more efficient than the state-of-the-art work.

Mining temporal characteristics of behaviors from interval events in e-learning

The XML support in relational databases and the SQL/XML language are still relatively new as compared to purely relational databases and traditional SQL. Today, most database users have a strong relational and SQL background. SQL/XML enables users to perform queries and updates across XML and relational data, but many struggle with writing SQL/XML statements or XQuery update expressions. One reason is the novelty of SQL/XML and of the XQuery expressions that must be included. Another problem is that the tree structure of the XML data may be unknown or difficult to understand for the user. Evolving XML Schemas as well as hybrid XML/relational schemas make it even harder to write SQL/XML statements. Also, legacy applications use SQL but may require access to XML data without costly code changes. Motivated by these challenges, we developed a method to generate SQL/XML query and update statements automatically. The input is either a GUI or a regular SQL statement that uses logical data item names irrespective of their actual location in relational or XML columns in the database. The output is a SQL/XML statement that queries or updates relational and XML data as needed to carry out the original user statement. This relieves the user and simplifies schema evolution and integration. We have prototyped and tested the proposed method on top of DB2 9.5.

Frequent itemset mining and association rule mining are fundamental problems in data mining. Despite of the intensive and continuous researches on frequent itemset mining, one essential and not completely solved drawback still remains. The drawback is pattern explosion: a huge number of frequent itemsets, or patterns, will be often derived. The association rule mining takes over the same drawback since each association rule consists of two frequent itemsets. One promising solution for alleviating this essential drawback practically as a post-processing is to rank the patterns and rules from various aspects and to identify which one to be examined first. In this paper, we propose to utilize the technology of representation learning to rank frequent itemsets and association rules. More specifically, we develop several evaluation measures for frequent itemsets and association rules by using the vector representations of items, transactions and frequent patterns which are obtained by existing representation learning techniques. By employing vector representations of three kinds of objects, various rankings reflecting different aspects can expect to be obtained.

Frequent itemsets, association rules and sequential patterns are defined on elements of power sets of items and reflect the many-to-many relationships among items. Although many tools have been developed to visualize association rules, none of them can simultaneously manage a large number of rules with multiple antecedents and multiple consequences. This problem is shown as a straightforward application of parallel coordinates. We show that, by properly arranging items on coordinates and by filtering out subsets of large frequent itemsets, item groups can be naturally displayed and that inherent properties such as partial orders in itemsets and in association rules are implied by this visualization paradigm. The usefulness of this approach is demonstrated through examples.

This paper describes the analysis of quantitative characteristics of frequent sets and association rules in the posts of Twitter microblogs related to different event discussions. For the analysis, we used a theory of frequent sets, association rules and a theory of formal concept analysis. We revealed the frequent sets and association rules which characterize the semantic relations between the concepts of analyzed subjects. The support of some frequent sets reaches its global maximum before the expected event but with some time delay. Such frequent sets may be considered as predictive markers that characterize the significance of expected events for blogosphere users. We showed that the time dynamics of confidence in some revealed association rules can also have predictive characteristics. Exceeding a certain threshold may be a signal for corresponding reaction in the society within the time interval between the maximum and the probable coming of an event. In this paper, we considered two types of events: the Olympic tennis tournament final in London, 2012 and the prediction of Eurovision 2013 winner.

This paper concentrates on how to mine useful information from massive XML documents in cloud computing environment. The structure of the Cloud computing and the corresponding tree data model of a XML document are analyzed in advance. Afterwards, structure of the proposed XML data mining system is illustrated, which is made up of three layers, such as “Application layer”, “Data processing layer”, and “XML Data converting layer”. In the XML Data converting layer, XML data are collected from databases and documents, and then the source data can be converted to XML file effectively. In the data processing layer, the process of data selection, cleaning and standardization for XML data set is implemented, moreover, a XML data set with higher degree of structure and rich semantics are obtained. In the application layer, “the results report module”, “data query module” and “results analysis module” are included. Next, massive XML data mining algorithm is proposed. The main innovations of this algorithm lie in that 1) the structure of a XML document is represented as an unordered tree, 2) the sub-structures of a XML document are modeled as sub-trees, and XML trees are regarded as a forest which is made up of all the sub-trees. Experimental results show that the proposed method can effectively mine useful information from massive XML documents in cloud computing environment with high efficiency.

Background: Knowledge discovering rare itemsets mining, and association rules are significant in a transactional dataset. Be that as it may, rare association rules are now and again more intriguing than frequent association rules since rare rules indicate an unforeseen or obscure association. The utilization of rare itemsets mining with item selector is unavoidable and has turned into an emerging field of research; therefore, this subject has numerous challenges. Objective: To perform the revenue examination of the marketing sector by rare itemsets selector by threshold and time series-based prediction technique. Methods: This paper gives the revenue examination of marketing sector by rare itemsets selector by threshold and time series-based prediction technique. A new algorithm for locating the rare itemsets by Association Rare itemset Rule Mining (ARIRM) to produced rules then utility itemsets discover by the threshold. When the rare patterns are analyzed, the ARIMA model used to anticipate the revenue. Based on the investigation of rare showcasing data with rules of the mining space, this methodology uses a tree structure to learn the rare items. Results: The test results in the "K" transactions with high revenues discovered utilizing the proposed model contrasted with other existing procedures; this forecast procedure is helpful for upcoming transactions. Conclusion: Based on the investigation of rare showcasing data with rules of the mining space, this methodology uses a tree structure to learn the rare items.

Most association rule mining techniques concentrate on finding frequent rules. However, rare association rules are in some cases more interesting than frequent association rules since rare rules represent unexpected or unknown associations. All current algorithms for rare association rule mining use an Apriori level-wise approach which has computationally expensive candidate generation and pruning steps. We propose RP-Tree, a method for mining a subset of rare association rules using a tree structure, and an information gain component that helps to identify the more interesting association rules. Empirical evaluation using a range of real world datasets shows that RP-Tree itemset and rule generation is more time efficient than modified versions of FP-Growth and ARIMA, and discovers 92-100% of all the interesting rare association rules. Additional evaluation using synthetic datasets also shows that RP-Tree is more efficient, in addtion to showing how the execution time of RP-Tree changes with transaction length and rare-item itemset size.

SufRec, an algorithm for mining association rules: Recursivity and task parallelism