Mining Architecture Research Articles

An Overlay-Based Data Mining Architecture Tolerant to Physical Network Disruptions

Management scheme for highly scalable big data mining has not been well studied in spite of the fact that big data mining provides many valuable and important information for us. An overlay-based parallel data mining architecture, which executes fully distributed data management and processing by employing the overlay network, can achieve high scalability. However, the overlay-based parallel mining architecture is not capable of providing data mining services in case of the physical network disruption that is caused by router/communication line breakdowns because numerous nodes are removed from the overlay network. To cope with this issue, this paper proposes an overlay network construction scheme based on node location in physical network, and a distributed task allocation scheme using overlay network technology. The numerical analysis indicates that the proposed schemes considerably outperform the conventional schemes in terms of service availability against physical network disruption.

Data Mining Architecture System Expansion

How to build a data warehouse and data mining is constantly worth exploring and optimization, not only technically, in commercial applications as well. This article discusses the introduction of new technologies and concepts, the traditional method of data warehouse technology has changed dramatically, based on data warehouse applications are a new development. Each enterprise data warehouse based on the characteristics of different companies, you can use a very flexible method of selection and design selection, implementation. According to some relatively new technical features, talk about data warehousing and data mining architecture.

Do We Need an Interdisciplinary Approach to Bring Consumers' Desires to e-Market?

In 2012, the internet advertising revenue in the United States of America reached a total of 36.6 billion dollars, a growth of 15.2% when compared to 2011. The efficiency of a marketing strategy relies in the ability to understand and to direct the consumers' desires. In this work, the authors propose an approach that combines the Internet-Based Information Consumer Theory (IBICT) with semiotics to bring consumers' desires to e-Market. Hence, we present IBICT's framework as a collective network set based on a semiotic human-machine approach. For implementation purposes, we propose a text mining architecture towards IBICT's framework, which leads to an IBICT's architecture, and an Interdisciplinary Research Project Management (IRPM) approach to determine IBICT's dimensions.

Survey paper on various mining methods on multimedia Images

Multimedia mining is a young but challenging subfield in data mining .Multimedia explanation represents an application of computer vision that presents the recognition of objects or ideas related to a multimedia document as a image. There is not unified conclusion in the concept, content and methods of Multimedia mining, Multimedia mining architecture and framework has to be further studied. there are various mining methods that we can apply on multimedia images like association rule mining, sequence mining, sequence pattern mining etc. In this survey paper we are focusing all this methods. We also discussed feature selection methods of various images.

An FPGA-Based Accelerator for Frequent Itemset Mining

In this article we describe a Field Programmable Gate Array (FPGA)-based coprocessor architecture for Frequent Itemset Mining (FIM). FIM is a common data mining task used to find frequently occurring subsets amongst a database of sets. FIM is a nonnumerical, data intensive computation and is used in machine learning and computational biology. FIM is particularly expensive---in terms of execution time and memory---when performed on large and/or sparse databases or when applied using a low appearance frequency threshold. Because of this, the development of increasingly efficient FIM algorithms and their mapping to parallel architectures is an active field. Previous attempts to accelerate FIM using FPGAs have relied on performance-limiting strategies such as iterative database loading and runtime logic unit reconfiguration. In this article, we present a novel architecture to implement Eclat, a well-known FIM algorithm. Unlike previous efforts, our technique does not impose limits on the maximum set size as a function of available FPGA logic resources and our design scales well to multiple FPGAs. In addition to a novel hardware design, we also present a corresponding compression scheme for intermediate results that are stored in on-chip memory. On a four-FPGA board, experimental results show up to 68X speedup compared to a highly optimized software implementation.

Impact of Duo-Mining in Knowledge Discovery Process

Duo mining is used frequently in a mixture of industries and its enduring to gain in both popularity and acceptance. Duo- Mining is basically a blend of data and text mining. This paper suggests Data mining architecture in addition with Knowledge discovery process. It also presents the comparison between data mining and text mining. As Data mining handles various processes like text Mining, Multi-media, Web mining etc.Text Classification, Clustering, Keyword based Association are the terms that are used to describe the process of Text Mining.

The Exploration of Data Mining in Face of Cloud Computing

This paper first discusses the limitations and shortcomings of data mining under the conditions of massive data. Combined with the advantages of cloud computing, a data mining architecture for cloud computing is designed, and on this basis, the paper discusses the improvement of data mining algorithms for cloud computing. As a theoretical exploration, the paper proposed useful suggestions for the optimization of data mining in face of cloud computing.

서비스 패턴 마이닝을 위한 컨텍스트 온톨로지 및 트리거 규칙 설계

Ubiquitous computing is a technique to provide users with appropriate services, collecting the context information in somewhere by attached sensor. An intelligent system needs to automatically update services according to the user`s various circumstances. To do this, in this paper, we propose a design of context ontology, trigger rule for mining service pattern related to users activity and an active mining architecture integrating trigger system. The proposed system is a framework for active mining user activity and service pattern by considering the relation between user context and object based on trigger system.

Mining Social Data to Extract Intellectual Knowledge

Social data mining is an interesting phe- nomenon which colligates different sources of social data to extract information. This information can be used in relationship prediction, decision making, pattern recognition, social mapping, responsibility distribution and many other applications. This paper presents a sys- tematical data mining architecture to mine intellectual knowledge from social data. In this research, we use social networking site facebook as primary data source. We collect different attributes such as about me, com- ments, wall post and age from facebook as raw data and use advanced data mining approaches to excavate intel- lectual knowledge. We also analyze our mined knowledge with comparison for possible usages like as human behavior prediction, pattern recognition, job responsibility distribution, decision making and product promoting.

Mining Social Data to Extract Intellectual Knowledge

Social data mining is an interesting phe-nomenon which colligates different sources of social data to extract information. This information can be used in relationship prediction, decision making, pat-tern recognition, social mapping, responsibility distri-bution and many other applications. This paper presents a systematical data mining architecture to mine intellectual knowledge from social data. In this research, we use social networking site facebook as primary data source. We collect different attributes such as about me, comments, wall post and age from facebook as raw data and use advanced data mining approaches to excavate intellectual knowledge. We also analyze our mined knowledge with comparison for possible usages like as human behavior prediction, pattern recognition, job responsibility distribution, decision making and product promoting.

Applying cluster-based fuzzy association rules mining framework into EC environment

During electronic commerce (EC) environment, how to effectively mine the useful transaction information will be an important issue to be addressed in designing the marketing strategy for most enterprises. Especially, the relationships between different databases (e.g., the transaction and online browsing database) may have the unknown and potential knowledge of business intelligence. Two important issues of mining association rules were mentioned to address EC application in this study. The first issue is the discovery of generalized fuzzy association rules in the transaction database. The second issue is to discover association rules from the web usage data and the large itemsets identified in the transaction database. A cluster-based fuzzy association rules (CBFAR) mining architecture is then proposed to simultaneously address such two issues in this study. Three contributions were achieved as: (a) an efficient fuzzy association rule miner based on cluster-based fuzzy-sets tables is presented to identify all the large fuzzy itemsets; (b) this approach requires less contrast to generate large itemsets; (3) a fuzzy rule mining approach is used to compute the confidence values for discovering the relationships between transaction database and browsing information database. Finally, a simulated example during EC environment is provided to demonstrate the rationality and feasibility of the proposed approach.

Design and Analysis of a Reconfigurable Platform for Frequent Pattern Mining

Frequent pattern mining algorithms are designed to find commonly occurring sets in databases. This class of algorithms is typically very memory intensive, leading to prohibitive runtimes on large databases. A class of reconfigurable architectures has been recently developed that have shown promise in accelerating some data mining applications. In this paper, we propose a new architecture for frequent pattern mining based on a systolic tree structure. The goal of this architecture is to mimic the internal memory layout of the original pattern mining software algorithm while achieving a higher throughput. We provide a detailed analysis of the area and performance requirements of our systolic tree-based architecture, and show that our reconfigurable platform is faster than the original software algorithm for mining long frequent patterns.

Weka‐STPM: a Software Architecture and Prototype for Semantic Trajectory Data Mining and Visualization

AbstractEnormous quantities of trajectory data are collected from many sources, such as GPS devices and mobile phones, as sequences of spatio‐temporal points. These data can be used in many application domains such as traffic management, urban planning, tourism, bird migration, and so on. Raw trajectory data, as generated by mobile devices have very little or no semantics, and in most applications a higher level of abstraction is needed to exploit these data for decision making. Although several different methods have been proposed so far for trajectory querying and mining, there are no software tools to help the end user with semantic trajectory data analysis. In this article we present a software architecture for semantic trajectory data mining as well as the first software prototype to enrich trajectory data with both semantic information and data mining. As a prototype we extend the Weka data mining toolkit with the module Weka‐STPM, which is interoperable with databases constructed under OGC specifications. We tested Weka‐STPM with real geographic databases, and trajectory data stored under the Postgresql/PostGIS DBMS.

Approximate Distributed K-Means Clustering over a Peer-to-Peer Network

Data intensive peer-to-peer (P2P) networks are finding increasing number of applications. Data mining in such P2P environments is a natural extension. However, common monolithic data mining architectures do not fit well in such environments since they typically require centralizing the distributed data which is usually not practical in a large P2P network. Distributed data mining algorithms that avoid large-scale synchronization or data centralization offer an alternate choice. This paper considers the distributed K-means clustering problem where the data and computing resources are distributed over a large P2P network. It offers two algorithms which produce an approximation of the result produced by the standard centralized K-means clustering algorithm. The first is designed to operate in a dynamic P2P network that can produce clusterings by ldquolocalrdquo synchronization only. The second algorithm uses uniformly sampled peers and provides analytical guarantees regarding the accuracy of clustering on a P2P network. Empirical results show that both the algorithms demonstrate good performance compared to their centralized counterparts at the modest communication cost.

A FRAMEWORK FOR TOOL-BASED SOFTWARE ARCHITECTURE RECONSTRUCTION

For software with nontrivial size and complexity, it is not feasible to manually perform architecture reconstruction. Therefore it is essential for the software architecture miner who is mining architecture from the existing software to have a well-defined software architecture reconstruction process that helps incorporate as much tool use as possible at the appropriate steps of architecture reconstruction. There are some existing software architecture reconstruction frameworks but they do not provide guidelines on how to systematically utilize tools to produce architecture views for a reconstruction purpose. In this paper, we propose a framework for tool-based software architecture reconstruction. This framework consists of a generic process for software architecture reconstruction and the steps to derive from it a concrete tool-based process to be used for actual architecture reconstruction. The architecture miner can use this framework to analyze source code for modifying source code as well as to reconstruct software architecture from source code.

Geometry-aware analysis of high-dimensional visual information sets

Over the past few decades we have been experiencing a data explosion; massive amounts of data are increasingly collected and multimedia databases, such as YouTube and Flickr, are rapidly expanding. At the same time rapid technological advancements in mobile devices and vision sensors have led to the emergence of novel multimedia mining architectures. These produce even more multimedia data, which are possibly captured under geometric transformations and need to be efficiently stored and analyzed. It is also common in such systems that data are collected distributively. This very fact poses great challenges in the design of effective methods for analysis and knowledge discovery from multimedia data. In this thesis, we study various instances of the problem of classification of visual data under the view-point of modern challenges. Roughly speaking, classification corresponds to the problem of categorizing an observed object to a particular class (or category), based on previously seen examples. We address important issues related to classification, namely flexible data representation for joint coding and classification, robust classification in the case of large geometric transformations and classification with multiple object observations in both centralized and distributed settings.

MI-MERCURY: A mobile agent architecture for ubiquitous retrieval and delivery of multimedia information

Mining multimedia information in the Web is in general an arduous task, due to the fact that, (a) humans perceive media content using high level concepts, (b) the subjective and vagueness of content interpretation, and (c) the fact that relevant data are often hidden in a huge amount of irrelevant information. In addition, delivering and distributing the retrieved information to a wide range of terminal devices of different properties over a wide range of networks to users of different preferences requires new tools and mechanisms for content transformation and adaptation. Other problems concern the language that the data are stored, which may not be the user's preferred language. To address these issues we propose an integrated, reconfigurable, adaptable and open architecture for mining, indexing and retrieving multimedia information based on a mobile agent technology scheme. The proposed architecture consists of three integral subsystems: the acquisition module, responsible for searching and retrieving media data (both textual and visual), the transformation module, able to adapt and transform the mined information to other forms of representation, and the distribution module for delivering and adapting the retrieved data in terms of terminal devices, network channels and user's preferences. The system is based on a reconfigurable architecture which is able to dynamically and automatically update the system response to user's actual needs and preferences, by extending descriptor classes that are considered more relevant by the users. New innovative algorithms are presented in this paper both at each system module as well as in the system integration. The system supports efficient content adaptation mechanisms, textual and visual summarization (both sequential and hierarchical), automatic language translation, ontological representation, visual processing and web-based data mining. Experimental analysis on real-life web sites has been performed to test the efficiency of the proposed scheme and compare it with other approaches presented in the literature.

Anteater: A Service-Oriented Architecture for High-Performance Data Mining

Data mining focuses on extracting useful information from large volumes of data, and thus has been the center of much attention in recent years. Building scalable, extensible, and easy-to-use data mining systems, however, has proved to be difficult. In response, the authors developed Anteater, a service-oriented architecture for data mining that relies on Web services to achieve extensibility and interoperability, offers simple abstractions for users, and supports computationally intensive processing on large amounts of data through massive parallelism

Pattern recognition in time series database: A case study on financial database

Today, there are more and more time series data that coexist with other data. These data exist in useful and understandable patterns. Data management of time series data must take into account an integrated approach. However, many researches face numeric data attributes. Therefore, the need for time series data mining tool has become extremely important. The purpose of this paper is to provide a novel pattern in mining architecture with mixed attributes that uses a systematic approach in the financial database information mining. Time series pattern mining (TSPM) architecture combines the extended visualization-induced self-organizing map algorithm and the extended Naive Bayesian algorithm. This mining architecture can simulate human intelligence and discover patterns automatically. The TSPM approach also demonstrates good returns in pattern research.

Automated trend analysis of proteomics data using an intelligent data mining architecture

Proteomics is a field dedicated to the analysis and identification of proteins within an organism. Within proteomics, two-dimensional electrophoresis (2-DE) is currently unrivalled as a technique to separate and analyse proteins from tissue samples. The analysis of post-experimental data produced from this technique has been identified as an important step within this overall process. Some of the long-term aims of this analysis are to identify targets for drug discovery and proteins associated with specific organism states. The large quantities of high-dimensional data produced from such experimentation requires expertise to analyse, which results in a processing bottleneck, limiting the potential of this approach. We present an intelligent data mining architecture that incorporates both data-driven and goal-driven strategies and is able to accommodate the spatial and temporal elements of the dataset under analysis. The architecture is able to automatically classify interesting proteins with a low number of false positives and false negatives. Using a data mining technique to detect variance within the data before classification offers performance advantages over other statistical variance techniques in the order of between 16 and 46%.