Algorithms for Extracting Frequent Episodes in the Process of Temporal Data Mining

An important aspect in the data mining process is the discovery of patterns having a great influence on the studied problem. The purpose of this paper is to study the frequent episodes data mining through the use of parallel pattern discovery algorithms. Parallel pattern discovery algorithms offer better performance and scalability, so they are of a great interest for the data mining research community. In the following, there will be highlighted some parallel and distributed frequent pattern mining algorithms on various platforms and it will also be presented a comparative study of their main features. The study takes into account the new possibilities that arise along with the emerging novel Compute Unified Device Architecture from the latest generation of graphics processing units. Based on their high performance, low cost and the increasing number of features offered, GPU processors are viable solutions for an optimal implementation of frequent pattern mining algorithmsFrequent Pattern Mining, Parallel Computing, Dynamic Load Balancing, Temporal Data Mining, CUDA, GPU, Fermi, Thread

High Performance Frequent Subgraph Mining on Transactional Datasets

Graph data mining has been a crucial as well as inevitable area of research. Large amounts of graph data are produced in many areas, such as Bioinformatics, Cheminformatics, Social Networks, and Web etc. Scalable graph data mining methods are getting increasingly popular and necessary due to increased graph complexities. Frequent subgraph mining is one such area where the task is to find overly recurring patterns/subgraphs. To tackle this problem, many main memory-based methods were proposed, which proved to be inefficient as the data size grew exponentially over time. In the past few years several research groups have attempted to handle the frequent subgraph mining (FSM) problem in multiple ways. Many authors have tried to achieve better performance using Graphic Processing Units (GPUs) which has multi-fold improvement over in-memory while dealing with large datasets. Later, Google\u27s MapReduce model with the Hadoop framework proved to be a major breakthrough in high performance large batch processing. Although MapReduce came with many benefits, its disk I/O and non-iterative style model could not help much for FSM domain since subgraph mining process is an iterative approach. In recent years, Spark has emerged to be the De Facto industry standard with its distributed in-memory computing capability. This is a right fit solution for iterative style of programming as well. In this work, we cover how high-performance computing has helped in improving the performance tremendously in the transactional directed and undirected aspect of graphs and performance comparisons of various FSM techniques are done based on experimental results

Frequent itemset mining on multiprocessor systems

Frequent itemset mining is an important building block in many data mining applications like market basket analysis, recommendation, web-mining, fraud detection, and gene expression analysis. In many of them, the datasets being mined can easily grow up to hundreds of gigabytes or even terabytes of data. Hence, efficient algorithms are required to process such large amounts of data. In recent years, there have been many frequent-itemset mining algorithms proposed, which however (1) often have high memory requirements and (2) do not exploit the large degrees of parallelism provided by modern multiprocessor systems. The high memory requirements arise mainly from inefficient data structures that have only been shown to be sufficient for small datasets. For large datasets, however, the use of these data structures force the algorithms to go out-of-core, i.e., they have to access secondary memory, which leads to serious performance degradations. Exploiting available parallelism is further required to mine large datasets because the serial performance of processors almost stopped increasing. Algorithms should therefore exploit the large number of available threads and also the other kinds of parallelism (e.g., vector instruction sets) besides thread-level parallelism. In this work, we tackle the high memory requirements of frequent itemset mining twofold: we (1) compress the datasets being mined because they must be kept in main memory during several mining invocations and (2) improve existing mining algorithms with memory-efficient data structures. For compressing the datasets, we employ efficient encodings that show a good compression performance on a wide variety of realistic datasets, i.e., the size of the datasets is reduced by up to 6.4x. The encodings can further be applied directly while loading the dataset from disk or network. Since encoding and decoding is repeatedly required for loading and mining the datasets, we reduce its costs by providing parallel encodings that achieve high throughputs for both tasks. For a memory-efficient representation of the mining algorithms’ intermediate data, we propose compact data structures and even employ explicit compression. Both methods together reduce the intermediate data’s size by up to 25x. The smaller memory requirements avoid or delay expensive out-of-core computation when large datasets are mined. For coping with the high parallelism provided by current multiprocessor systems, we identify the performance hot spots and scalability issues of existing frequent-itemset mining algorithms. The hot spots, which form basic building blocks of these algorithms, cover (1) counting the frequency of fixed-length strings, (2) building prefix trees, (3) compressing integer values, and (4) intersecting lists of sorted integer values or bitmaps. For all of them, we discuss how to exploit available parallelism and provide scalable solutions. Furthermore, almost all components of the mining algorithms must be parallelized to keep the sequential fraction of the algorithms as small as possible. We integrate the parallelized building blocks and components into three well-known mining algorithms and further analyze the impact of certain existing optimizations. Our algorithms are already single-threaded often up an order of magnitude faster than existing highly optimized algorithms and further scale almost linear on a large 32-core multiprocessor system. Although our optimizations are intended for frequent-itemset mining algorithms, they can be applied with only minor changes to algorithms that are used for mining of other types of itemsets

Enabling parallelism and optimizations in data mining algorithms for power-law data

Today's data mining tasks aim to extract meaningful information from a large amount of data in a reasonable time mainly via means of --- a) algorithmic advances, such as fast approximate algorithms and efficient learning algorithms, and b) architectural advances, such as machines with massive compute capacity involving distributed multi-core processors and high throughput accelerators. For current and future generation processors, parallel algorithms are critical for fully utilizing computing resources. Furthermore, exploiting data properties for performance gain becomes crucial for data mining applications. In this work, we focus our attention on power-law behavior –-- a common property found in a large class of data, such as text data, internet traffic, and click-stream data. Specifically, we address the following questions in the context of power-law data: How well do the critical data mining algorithms of current interest fit with today's parallel architectures? Which algorithmic and mapping opportunities can be leveraged to further improve performance?, and What are the relative challenges and gains for such approaches? Specifically, we first investigate the suitability of the "frequency estimation" problem for GPU-scale parallelism. Sketching algorithms are a popular choice for this task due to their desirable trade-off between estimation accuracy and space-time efficiency. However, most of the past work on sketch-based frequency estimation focused on CPU implementations. In our work, we propose a novel approach for sketches, which exploits the natural skewness in the power-law data to efficiently utilize the massive amounts of parallelism in modern GPUs. Next, we explore the problem of "identifying top-K frequent elements" for distributed data streams on modern distributed settings with both multi-core and multi-node CPU parallelism. Sketch-based approaches, such as Count-Min Sketch (CMS) with top-K heap, have an excellent update time but lacks the important property of reducibility, which is needed for exploiting data parallelism. On the other end, the popular Frequent Algorithm (FA) leads to reducible summaries, but its update costs are high. Our approach Topkapi, gives the best of both worlds, i.e., it is reducible like FA and has an efficient update time similar to CMS. For power-law data, Topkapi possesses strong theoretical guarantees and leads to significant performance gains, relative to past work. Finally, we study Word2Vec, a popular word embedding method widely used in Machine learning and Natural Language Processing applications, such as machine translation, sentiment analysis, and query answering. This time, we target Single Instruction Multiple Data (SIMD) parallelism. With the increasing vector lengths in commodity CPUs, such as AVX-512 with a vector length of 512 bits, efficient vector processing unit utilization becomes a major performance game-changer. By employing a static multi-version code generation strategy coupled with an algorithmic approximation based on the power-law frequency distribution of words, we achieve significant reductions in training time relative to the state-of-the-art.Ph.D

Parallel Methods for Mining Frequent Sequential patterns

The explosive growth of data and the rapid progress of technology have led to a huge amount of data that is collected every day. In that data volume contains much valuable information. Data mining is the emerging field of applying statistical and artificial intelligence techniques to the problem of finding novel, useful and non-trivial patterns from large databases. It is the task of discovering interesting patterns from large amounts of data. This is achieved by determining both implicit and explicit unidentified patterns in data that can direct the process of decision making. There are many data mining tasks, such as classification, clustering, association rule mining and sequential pattern mining. In that, sequential pattern mining is an important problem in data mining. It provides an effective way to analyze the sequence data. The goal of sequential pattern mining is to discover interesting, unexpected and useful patterns from sequence databases. This task is used in many wide applications such as financial data analysis of banks, retail industry, customer shopping history, goods transportation, consumption and services, telecommunication industry, biological data analysis, scientific applications, network intrusion detection, scientific research, etc. Different types of sequential pattern mining can be performed, they are sequential patterns, maximal sequential patterns, closed sequences, constraint based and time interval based sequential patterns. Sequential pattern mining refers to the identification of frequent subsequences in sequence databases as patterns. In the last two decades, researchers have proposed many techniques and algorithms for extracting the frequent sequential patterns, in which the downward closure property plays a fundamental role. Sequential pattern is a sequence of itemsets that frequently occur in a specific order, where all items in the same itemsets are supposed to have the same transaction time value. One of the challenges for sequential pattern mining is the computational costs beside that is the potentially huge number of extracted patterns. In this thesis, we present an overview of the work done for sequential pattern mining and develop parallel methods for mining frequent sequential patterns in sequence databases that can tackle emerging data processing workloads while coping with larger and larger scales.The explosive growth of data and the rapid progress of technology have led to a huge amount of data that is collected every day. In that data volume contains much valuable information. Data mining is the emerging field of applying statistical and artificial intelligence techniques to the problem of finding novel, useful and non-trivial patterns from large databases. It is the task of discovering interesting patterns from large amounts of data. This is achieved by determining both implicit and explicit unidentified patterns in data that can direct the process of decision making. There are many data mining tasks, such as classification, clustering, association rule mining and sequential pattern mining. In that, sequential pattern mining is an important problem in data mining. It provides an effective way to analyze the sequence data. The goal of sequential pattern mining is to discover interesting, unexpected and useful patterns from sequence databases. This task is used in many wide applications such as financial data analysis of banks, retail industry, customer shopping history, goods transportation, consumption and services, telecommunication industry, biological data analysis, scientific applications, network intrusion detection, scientific research, etc. Different types of sequential pattern mining can be performed, they are sequential patterns, maximal sequential patterns, closed sequences, constraint based and time interval based sequential patterns. Sequential pattern mining refers to the identification of frequent subsequences in sequence databases as patterns. In the last two decades, researchers have proposed many techniques and algorithms for extracting the frequent sequential patterns, in which the downward closure property plays a fundamental role. Sequential pattern is a sequence of itemsets that frequently occur in a specific order, where all items in the same itemsets are supposed to have the same transaction time value. One of the challenges for sequential pattern mining is the computational costs beside that is the potentially huge number of extracted patterns. In this thesis, we present an overview of the work done for sequential pattern mining and develop parallel methods for mining frequent sequential patterns in sequence databases that can tackle emerging data processing workloads while coping with larger and larger scales.460 - Katedra informatikyvyhově

Rule Mining and Sequential Pattern Based Predictive Modeling with EMR Data

Electronic medical record (EMR) data is collected on a daily basis at hospitals and other healthcare facilities to track patients’ health situations including conditions, treatments (medications, procedures), diagnostics (labs) and associated healthcare operations. Besides being useful for individual patient care and hospital operations (e.g., billing, triaging), EMRs can also be exploited for secondary data analyses to glean discriminative patterns that hold across patient cohorts for different phenotypes. These patterns in turn can yield high level insights into disease progression with interventional potential. In this dissertation, using a large scale realistic EMR dataset of over one million patients visiting University of Kentucky healthcare facilities, we explore data mining and machine learning methods for association rule (AR) mining and predictive modeling with mood and anxiety disorders as use-cases. Our first work involves analysis of existing quantitative measures of rule interestingness to assess how they align with a practicing psychiatrist’s sense of novelty/surprise corresponding to ARs identified from EMRs. Our second effort involves mining causal ARs with depression and anxiety disorders as target conditions through matching methods accounting for computationally identified confounding attributes. Our final effort involves efficient implementation (via GPUs) and application of contrast pattern mining to predictive modeling for mental conditions using various representational methods and recurrent neural networks. Overall, we demonstrate the effectiveness of rule mining methods in secondary analyses of EMR data for identifying causal associations and building predictive models for diseases

Analytical Query Execution Optimized for all Layers of Modern Hardware

Analytical database queries are at the core of business intelligence and decision support. To analyze the vast amounts of data available today, query execution needs to be orders of magnitude faster. Hardware advances have made a profound impact on database design and implementation. The large main memory capacity allows queries to execute exclusively in memory and shifts the bottleneck from disk access to memory bandwidth. In the new setting, to optimize query performance, databases must be aware of an unprecedented multitude of complicated hardware features. This thesis focuses on the design and implementation of highly efficient database systems by optimizing analytical query execution for all layers of modern hardware. The hardware layers include the network across multiple machines, main memory and the NUMA interconnection across multiple processors, the multiple levels of caches across multiple processor cores, and the execution pipeline within each core. For the network layer, we introduce a distributed join algorithm that minimizes the network traffic. For the memory hierarchy, we describe partitioning variants aware to the dynamics of the CPU caches and the NUMA interconnection. To improve the memory access rate of linear scans, we optimize lightweight compression variants and evaluate their trade-offs. To accelerate query execution within the core pipeline, we introduce advanced SIMD vectorization techniques generalizable across multiple operators. We evaluate our algorithms and techniques on both mainstream hardware and on many-integrated-core platforms, and combine our techniques in a new query engine design that can better utilize the features of many-core CPUs. In the era of hardware becoming increasingly parallel and datasets consistently growing in size, this thesis can serve as a compass for developing hardware-conscious databases with truly high-performance analytical query execution

Failure avoidance techniques for HPC systems based on failure prediction

A increasingly larger percentage of computing capacity in today's large high-performance computing systems is wasted due to failures and recoveries. Moreover, it is expected that high performance computing will reach exascale within a decade, decreasing the mean time between failures to one day or even a few hours, making fault tolerance a major challenge for the HPC community. As a consequence, current research is focusing on providing fault tolerance strategies that aim to minimize fault's effects on applications. By far, the most popular and used techniques from this field are rollback-recovery protocols. However, existing rollback-recovery techniques have severe scalability limitations and without further optimizations the use of current protocols is put under serious questions for future exascale systems. A way of reducing the overhead induced by these strategies is by combining them with failure avoidance methods. Failure avoidance is based on a prediction model that detects fault occurrences ahead of time and allows preventive measures to be taken, such as task migration or checkpointing the application before failure. The same methodology can be generalized and applied to anomaly avoidance, where anomaly can mean anything from system failures to performance degradation at the application level. For this, monitoring systems require a reliable prediction system to give information on when failures will occur and at what location. Thus far, research in this field used ideal predictors that do not have any implementation in real HPC systems. This thesis focuses on analyzing and characterizing anomaly patterns at both the application and system levels and on offering solutions to prevent anomalies from affecting applications running in the system. Currently, there is no good characterization of normal behavior for system state data or how different components react to failures within HPC systems. For example, in case a node experiences a network failure and is incapable of generating log messages, the failure is announced in the log files by a lack of generated messages. Conversely, some component failures may cause logging a large numbers of notifications. For example, memory failures can result in a single faulty component generating hundreds or thousands of messages in less than a day. It is important to be able to capture the behavior of each event type and understand what is the normal behavior and how each failure type affects it. This idea represents the building block of a novel way of characterizing the state of the system in time by analyzing the properties of each event described in different system metrics, considering its own trend and behavior. The method introduces the integration between signal processing concepts and data mining techniques in the context of analysis for large-scale systems. By shaping the normal and faulty behavior of each event and of the whole system, appropriate models and methods for descriptive and forecasting purposes are proposed. After having an accurate overview of the whole system, the thesis analyzes how the prediction model impacts current fault tolerance techniques and in the end integrates it into a fault avoidance solution. This hybrid protocol optimizes the overhead that current fault tolerance strategies impose on applications and presents a viable solution for future large-scale systems

Towards Name Disambiguation: Relational, Streaming, and Privacy-Preserving Text Data

In the real world, our DNA is unique but many people share names. This phenomenon often causes erroneous aggregation of documents of multiple persons who are namesakes of one another. Such mistakes deteriorate the performance of document retrieval, web search, and more seriously, cause improper attribution of credit or blame in digital forensics. To resolve this issue, the name disambiguation task 1 is designed to partition the documents associated with a name reference such that each partition contains documents pertaining to a unique real-life person. Existing algorithms for this task mainly suffer from the following drawbacks. First, the majority of existing solutions substantially rely on feature engineering, such as biographical feature extraction, or construction of auxiliary features from Wikipedia. However, for many scenarios, such features may be costly to obtain or unavailable in privacy sensitive domains. Instead we solve the name disambiguation task in restricted setting by leveraging only the relational data in the form of anonymized graphs. Second, most of the existing works for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task should be performed in an online streaming fashion in order to identify records of new ambiguous entities having no preexisting records. Finally, we investigate the potential disclosure risk of textual features used in name disambiguation and propose several algorithms to tackle the task in a privacy-aware scenario. In summary, in this dissertation, we present a number of novel approaches to address name disambiguation tasks from the above three aspects independently, namely relational, streaming, and privacy preserving textual data