Mining Recent Frequent Itemsets in Sliding Windows over Data Streams

This paper considers the problem of mining recent frequent itemsets over data streams. As the data grows without limit at a rapid rate, it is hard to track the new changes of frequent itemsets over data streams. We propose an efficient one-pass algorithm in sliding windows over data streams with an error bound guarantee. This algorithm does not need to refer to obsolete transactions when they are removed from the sliding window. It exploits a compact data structure to maintain potentially frequent itemsets so that it can output recent frequent itemsets at any time. Flexible queries for continuous transactions in the sliding window can be answered with an error bound guarantee

Analysis and acceleration of data mining algorithms on high performance reconfigurable computing platforms

With the continued development of computation and communication technologies, we are overwhelmed with electronic data. Ubiquitous data in governments, commercial enterprises, universities and various organizations records our decisions, transactions and thoughts. The data collection rate is undergoing tremendous increase. And there is no end in sight. On one hand, as the volume of data explodes, the gap between the human being\u27s understanding of the data and the knowledge hidden in the data will be enlarged. The algorithms and techniques, collectively known as data mining, are emerged to bridge the gap. The data mining algorithms are usually data-compute intensive. On the other hand, the overall computing system performance is not increasing at an equal rate. Consequently, there is strong requirement to design special computing systems to accelerate data mining applications. FPGAs based High Performance Reconfigurable Computing(HPRC) system is to design optimized hardware architecture for a given problem. The increased gate count, arithmetic capability, and other features of modern FPGAs now allow researcher to implement highly complicated reconfigurable computational architecture. In contrast with ASICs, FPGAs have the advantages of low power, low nonrecurring engineering costs, high design flexibility and the ability to update functionality after shipping. In this thesis, we first design the architectures for data intensive and data-compute intensive applications respectively. Then we present a general HPRC framework for data mining applications: Frequent Pattern Mining(FPM) is a data-compute intensive application which is to find commonly occurring itemsets in databases. We use systolic tree architecture in FPGA hardware to mimic the internal memory layout of FP-growth algorithm while achieving higher throughput. The experimental results demonstrate that the proposed hardware architecture is faster than the software approach. Sparse Matrix-Vector Multiplication(SMVM) is a data-intensive application which is an important computing core in many applications. We present a scalable and efficient FPGA-based SMVM architecture which can handle arbitrary matrix sizes without preprocessing or zero padding and can be dynamically expanded based on the available I/O bandwidth. The experimental results using a commercial FPGA-based acceleration system demonstrate that our reconfigurable SMVM engine is more efficient than existing state-of-the-art, with speedups over a highly optimized software implementation of 2.5X to 6.5X, depending on the sparsity of the input benchmark. Accelerating Text Classification Using SMVM is performed in Convey HC-1 HPRC platform. The SMVM engines are deployed into multiple FPGA chips. Text documents are represented as large sparse matrices using Vector Space Model(VSM). The k-nearest neighbor algorithm uses SMVM to perform classification simultaneously on multiple FPGAs. Our experiment shows that the classification in Convey HC-1 is several times faster compared with the traditional computing architecture. MapReduce Reconfigurable Framework for Data Mining Applications is a pipelined and high performance framework for FPGA design based on the MapReduce model. Our goal is to lessen the FPGA programmer burden while minimizing performance degradation. The designer only need focus on the mapper and reducer modules design. We redesigned the SMVM architecture using the MapReduce Framework. The manual VHDL code is only 15 percent of that used in the customized architecture

Modelling epistasis in genetic disease using Petri nets, evolutionary computation and frequent itemset mining

Petri nets are useful for mathematically modelling disease-causing genetic epistasis. A Petri net model of an interaction has the potential to lead to biological insight into the cause of a genetic disease. However, defining a Petri net by hand for a particular interaction is extremely difficult because of the sheer complexity of the problem and degrees of freedom inherent in a Petri net’s architecture. We propose therefore a novel method, based on evolutionary computation and data mining, for automatically constructing Petri net models of non-linear gene interactions. The method comprises two main steps. Firstly, an initial partial Petri net is set up with several repeated sub-nets that model individual genes and a set of constraints, comprising relevant common sense and biological knowledge, is also defined. These constraints characterise the class of Petri nets that are desired. Secondly, this initial Petri net structure and the constraints are used as the input to a genetic algorithm. The genetic algorithm searches for a Petri net architecture that is both a superset of the initial net, and also conforms to all of the given constraints. The genetic algorithm evaluation function that we employ gives equal weighting to both the accuracy of the net and also its parsimony. We demonstrate our method using an epistatic model related to the presence of digital ulcers in systemic sclerosis patients that was recently reported in the literature. Our results show that although individual “perfect” Petri nets can frequently be discovered for this interaction, the true value of this approach lies in generating many different perfect nets, and applying data mining techniques to them in order to elucidate common and statistically significant patterns of interaction

Data mining by means of generalized patterns

The thesis is mainly focused on the study and the application of pattern discovery algorithms that aggregate database knowledge to discover and exploit valuable correlations, hidden in the analyzed data, at different abstraction levels. The aim of the research effort described in this work is two-fold: the discovery of associations, in the form of generalized patterns, from large data collections and the inference of semantic models, i.e., taxonomies and ontologies, suitable for driving the mining proces

GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY

The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial portion are not much significant for many data analysis endeavours. Due to this, the discovery of a small number of interesting patterns from the exponentially large number of frequent patterns according to a particular user\u27s interest is an important task. Existing works on patter

Dynamic partial reconfiguration management for high performance and reliability in FPGAs

Modern Field-Programmable Gate Arrays (FPGAs) are no longer used to implement small “glue logic” circuitries. The high-density of reconfigurable logic resources in today’s FPGAs enable the implementation of large systems in a single chip. FPGAs are highly flexible devices; their functionality can be altered by simply loading a new binary file in their configuration memory. While the flexibility of FPGAs is comparable to General-Purpose Processors (GPPs), in the sense that different functions can be performed using the same hardware, the performance gain that can be achieved using FPGAs can be orders of magnitudes higher as FPGAs offer the ability for customisation of parallel computational architectures. Dynamic Partial Reconfiguration (DPR) allows for changing the functionality of certain blocks on the chip while the rest of the FPGA is operational. DPR has sparked the interest of researchers to explore new computational platforms where computational tasks are off-loaded from a main CPU to be executed using dedicated reconfigurable hardware accelerators configured on demand at run-time. By having a battery of custom accelerators which can be swapped in and out of the FPGA at runtime, a higher computational density can be achieved compared to static systems where the accelerators are bound to fixed locations within the chip. Furthermore, the ability of relocating these accelerators across several locations on the chip allows for the implementation of adaptive systems which can mitigate emerging faults in the FPGA chip when operating in harsh environments. By porting the appropriate fault mitigation techniques in such computational platforms, the advantages of FPGAs can be harnessed in different applications in space and military electronics where FPGAs are usually seen as unreliable devices due to their sensitivity to radiation and extreme environmental conditions. In light of the above, this thesis investigates the deployment of DPR as: 1) a method for enhancing performance by efficient exploitation of the FPGA resources, and 2) a method for enhancing the reliability of systems intended to operate in harsh environments. Achieving optimal performance in such systems requires an efficient internal configuration management system to manage the reconfiguration and execution of the reconfigurable modules in the FPGA. In addition, the system needs to support “fault-resilience” features by integrating parameterisable fault detection and recovery capabilities to meet the reliability standard of fault-tolerant applications. This thesis addresses all the design and implementation aspects of an Internal Configuration Manger (ICM) which supports a novel bitstream relocation model to enable the placement of relocatable accelerators across several locations on the FPGA chip. In addition to supporting all the configuration capabilities required to implement a Reconfigurable Operating System (ROS), the proposed ICM also supports the novel multiple-clone configuration technique which allows for cloning several instances of the same hardware accelerator at the same time resulting in much shorter configuration time compared to traditional configuration techniques. A faulttolerant (FT) version of the proposed ICM which supports a comprehensive faultrecovery scheme is also introduced in this thesis. The proposed FT-ICM is designed with a much smaller area footprint compared to Triple Modular Redundancy (TMR) hardening techniques while keeping a comparable level of fault-resilience. The capabilities of the proposed ICM system are demonstrated with two novel applications. The first application demonstrates a proof-of-concept reliable FPGA server solution used for executing encryption/decryption queries. The proposed server deploys bitstream relocation and modular redundancy to mitigate both permanent and transient faults in the device. It also deploys a novel Built-In Self- Test (BIST) diagnosis scheme, specifically designed to detect emerging permanent faults in the system at run-time. The second application is a data mining application where DPR is used to increase the computational density of a system used to implement the Frequent Itemset Mining (FIM) problem

A Constraint-based Querying System for Exploratory Pattern Discovery

In this article we present CONQUEST, a constraint-based querying system able to support the intrinsically exploratory (i.e., human-guided, interactive and iterative) nature of pattern discovery. Following the inductive database vision, our framework provides users with an expressive constraint-based query language, which allows the discovery process to be effectively driven toward potentially interesting patterns. Such constraints are also exploited to reduce the cost of pattern mining computation. CONQUEST is a comprehensive mining system that can access real-world relational databases from which to extract data. Through the interaction with a friendly graphical user interface (GUI), the user can define complex mining queries by means of few clicks. After a pre-processing step, mining queries are answered by an efficient and robust pattern mining engine which entails the state-of-the-art of data and search space reduction techniques. Resulting patterns are then presented to the user in a pattern browsing window, and possibly stored back in the underlying database as relations

Data mining techniques for complex application domains

The emergence of advanced communication techniques has increased availability of large collection of data in electronic form in a number of application domains including healthcare, e- business, and e-learning. Everyday a large amount of records are stored electronically. However, finding useful information from such a large data collection is a challenging issue. Data mining technology aims automatically extracting hidden knowledge from large data repositories exploiting sophisticated algorithms. The hidden knowledge in the electronic data may be potentially utilized to facilitate the procedures, productivity, and reliability of several application domains. The PhD activity has been focused on novel and effective data mining approaches to tackle the complex data coming from two main application domains: Healthcare data analysis and Textual data analysis. The research activity, in the context of healthcare data, addressed the application of different data mining techniques to discover valuable knowledge from real exam-log data of patients. In particular, efforts have been devoted to the extraction of medical pathways, which can be exploited to analyze the actual treatments followed by patients. The derived knowledge not only provides useful information to deal with the treatment procedures but may also play an important role in future predictions of potential patient risks associated with medical treatments. The research effort in textual data analysis is twofold. On the one hand, a novel approach to discovery of succinct summaries of large document collections has been proposed. On the other hand, the suitability of an established descriptive data mining to support domain experts in making decisions has been investigated. Both research activities are focused on adopting widely exploratory data mining techniques to textual data analysis, which require overcoming intrinsic limitations for traditional algorithms for handling textual documents efficiently and effectively

From sequential patterns to concurrent branch patterns: a new post sequential patterns mining approach

A thesis submitted for the degree of Doctor ofPhilosophy of the University of BedfordshireSequential patterns mining is an important pattern discovery technique used to identify frequently observed sequential occurrence of items across ordered transactions over time. It has been intensively studied and there exists a great diversity of algorithms. However, there is a major problem associated with the conventional sequential patterns mining in that patterns derived are often large and not very easy to understand or use. In addition, more complex relations among events are often hidden behind sequences. A novel model for sequential patterns called Sequential Patterns Graph (SPG) is proposed. The construction algorithm of SPG is presented with experimental results to substantiate the concept. The thesis then sets out to define some new structural patterns such as concurrent branch patterns, exclusive patterns and iterative patterns which are generally hidden behind sequential patterns. Finally, an integrative framework, named Post Sequential Patterns Mining (PSPM), which is based on sequential patterns mining, is also proposed for the discovery and visualisation of structural patterns. This thesis is intended to prove that discrete sequential patterns derived from traditional sequential patterns mining can be modelled graphically using SPG. It is concluded from experiments and theoretical studies that SPG is not only a minimal representation of sequential patterns mining, but it also represents the interrelation among patterns and establishes further the foundation for mining structural knowledge (i.e. concurrent branch patterns, exclusive patterns and iterative patterns). from experiments conducted on both synthetic and real datasets, it is shown that Concurrent Branch Patterns (CBP) mining is an effective and efficient mining algorithm suitable for concurrent branch patterns