Twitter data analysis by means of Strong Flipping Generalized Itemsets

Twitter data has recently been considered to perform a large variety of advanced analysis. Analysis ofTwitter data imposes new challenges because the data distribution is intrinsically sparse, due to a large number of messages post every day by using a wide vocabulary. Aimed at addressing this issue, generalized itemsets - sets of items at different abstraction levels - can be effectively mined and used todiscover interesting multiple-level correlations among data supplied with taxonomies. Each generalizeditemset is characterized by a correlation type (positive, negative, or null) according to the strength of thecorrelation among its items.This paper presents a novel data mining approach to supporting different and interesting targetedanalysis - topic trend analysis, context-aware service profiling - by analyzing Twitter posts. We aim atdiscovering contrasting situations by means of generalized itemsets. Specifically, we focus on comparingitemsets discovered at different abstraction levels and we select large subsets of specific (descendant)itemsets that show correlation type changes with respect to their common ancestor. To this aim, a novelkind of pattern, namely the Strong Flipping Generalized Itemset (SFGI), is extracted from Twitter mes-sages and contextual information supplied with taxonomy hierarchies. Each SFGI consists of a frequentgeneralized itemset X and the set of its descendants showing a correlation type change with respect to X. Experiments performed on both real and synthetic datasets demonstrate the effectiveness of the pro-posed approach in discovering interesting and hidden knowledge from Twitter dat

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

Content sensitivity based access control model for big data

Big data technologies have seen tremendous growth in recent years. They are being widely used in both industry and academia. In spite of such exponential growth, these technologies lack adequate measures to protect the data from misuse or abuse. Corporations that collect data from multiple sources are at risk of liabilities due to exposure of sensitive information. In the current implementation of Hadoop, only file level access control is feasible. Providing users, the ability to access data based on attributes in a dataset or based on their role is complicated due to the sheer volume and multiple formats (structured, unstructured and semi-structured) of data. In this dissertation an access control framework, which enforces access control policies dynamically based on the sensitivity of the data is proposed. This framework enforces access control policies by harnessing the data context, usage patterns and information sensitivity. Information sensitivity changes over time with the addition and removal of datasets, which can lead to modifications in the access control decisions and the proposed framework accommodates these changes. The proposed framework is automated to a large extent and requires minimal user intervention. The experimental results show that the proposed framework is capable of enforcing access control policies on non-multimedia datasets with minimal overhea

A recommender system for e-retail

The e-retail sector in South Africa has a significant opportunity to capture a large portion of the country's retail industry. Central to seizing this opportunity is leveraging the advantages that the online setting affords. In particular, the e-retailer can offer an extremely large catalogue of products; far beyond what a traditional retailer is capable of supporting. However, as the catalogue grows, it becomes increasingly difficult for a customer to efficiently discover desirable products. As a consequence, it is important for the e-retailer to develop tools that automatically explore the catalogue for the customer. In this dissertation, we develop a recommender system (RS), whose purpose is to provide suggestions for products that are most likely of interest to a particular customer. There are two primary contributions of this dissertation. First, we describe a set of six characteristics that all effective RS's should possess, namely; accuracy, responsiveness, durability, scalability, model management, and extensibility. Second, we develop an RS that is capable of serving recommendations in an actual e-retail environment. The design of the RS is an attempt to embody the characteristics mentioned above. In addition, to show how the RS supports model selection, we present a proof-of-concept experiment comparing two popular methods for generating recommendations that we implement for this dissertation, namely, implicit matrix factorisation (IMF) and Bayesian personalised ranking (BPR)

On the enhancement of Big Data Pipelines through Data Preparation, Data Quality, and the distribution of Optimisation Problems

Nowadays, data are fundamental for companies, providing operational support by facilitating daily transactions. Data has also become the cornerstone of strategic decision-making processes in businesses. For this purpose, there are numerous techniques that allow to extract knowledge and value from data. For example, optimisation algorithms excel at supporting decision-making processes to improve the use of resources, time and costs in the organisation. In the current industrial context, organisations usually rely on business processes to orchestrate their daily activities while collecting large amounts of information from heterogeneous sources. Therefore, the support of Big Data technologies (which are based on distributed environments) is required given the volume, variety and speed of data. Then, in order to extract value from the data, a set of techniques or activities is applied in an orderly way and at different stages. This set of techniques or activities, which facilitate the acquisition, preparation, and analysis of data, is known in the literature as Big Data pipelines. In this thesis, the improvement of three stages of the Big Data pipelines is tackled: Data Preparation, Data Quality assessment, and Data Analysis. These improvements can be addressed from an individual perspective, by focussing on each stage, or from a more complex and global perspective, implying the coordination of these stages to create data workflows. The first stage to improve is the Data Preparation by supporting the preparation of data with complex structures (i.e., data with various levels of nested structures, such as arrays). Shortcomings have been found in the literature and current technologies for transforming complex data in a simple way. Therefore, this thesis aims to improve the Data Preparation stage through Domain-Specific Languages (DSLs). Specifically, two DSLs are proposed for different use cases. While one of them is a general-purpose Data Transformation language, the other is a DSL aimed at extracting event logs in a standard format for process mining algorithms. The second area for improvement is related to the assessment of Data Quality. Depending on the type of Data Analysis algorithm, poor-quality data can seriously skew the results. A clear example are optimisation algorithms. If the data are not sufficiently accurate and complete, the search space can be severely affected. Therefore, this thesis formulates a methodology for modelling Data Quality rules adjusted to the context of use, as well as a tool that facilitates the automation of their assessment. This allows to discard the data that do not meet the quality criteria defined by the organisation. In addition, the proposal includes a framework that helps to select actions to improve the usability of the data. The third and last proposal involves the Data Analysis stage. In this case, this thesis faces the challenge of supporting the use of optimisation problems in Big Data pipelines. There is a lack of methodological solutions that allow computing exhaustive optimisation problems in distributed environments (i.e., those optimisation problems that guarantee the finding of an optimal solution by exploring the whole search space). The resolution of this type of problem in the Big Data context is computationally complex, and can be NP-complete. This is caused by two different factors. On the one hand, the search space can increase significantly as the amount of data to be processed by the optimisation algorithms increases. This challenge is addressed through a technique to generate and group problems with distributed data. On the other hand, processing optimisation problems with complex models and large search spaces in distributed environments is not trivial. Therefore, a proposal is presented for a particular case in this type of scenario. As a result, this thesis develops methodologies that have been published in scientific journals and conferences.The methodologies have been implemented in software tools that are integrated with the Apache Spark data processing engine. The solutions have been validated through tests and use cases with real datasets

A Platform for Scalable Low-Latency Analytics using MapReduce

Today, the ability to process big data has become crucial to the information needs of many enterprise businesses, scientific applications, and governments. Recently, there have been increasing needs of processing data that is not only big but also fast . Here fast data refers to high-speed real-time and near real-time data streams, such as Twitter feeds, search query streams, click streams, impressions, and system logs. To handle both historical data and real-time data, many companies have to maintain multiple systems. However, recent real-world case studies show that maintaining multiple systems cause not only code duplication, but also intensive manual work to partition the analytics workloads and determine which data is processed by which system. These issues point to the need for a general, unified data processing framework to support analytical queries with different latency requirements. This thesis takes a further step towards building a general, unified system for big and fast data analytics. In order to build such a system, I propose to build on existing solutions on data parallelism and extend them with two new features: incremental processing and stream processing with latency constraints. This thesis starts with Hadoop, the most popular open-source MapReduce implementation, which provides proven scalability based on data parallelism. I answer the following questions: (1) Is Hadoop able to support incremental processing? (2) What are the necessary architecture changes in order to support incremental processing? (3) What are the additional design features needed to support stream processing with latency constraints? The thesis includes three parts that answer each of the questions. The first part of the thesis validates whether the existing MapReduce implementations can support incremental processing. Incremental processing means that computation is performed as soon as the relevant data becomes available. My extensive benchmark study of Hadoop-based MapReduce systems shows that the widely-used sort-merge implementation for partitioning and parallel processing poses a fundamental barrier to incremental computation. I further propose a cost model, and optimize the Hadoop system configuration based on the model. The benchmark results over the optimized system verify that the barrier to incremental computation is intrinsic, and cannot be removed by tuning system parameters. In the second part of the thesis, I employ various purely hash-based techniques to enable fast in-memory incremental processing in MapReduce, and frequent key based techniques to extend such processing to workloads that require memory more than available. I evaluate my Hadoop-based prototype equipped with all proposed techniques. The results show that the hash techniques allow the reduce progress to keep up with the map progress with up to 3 orders of magnitude reduction of internal disk spills, and enable results to be returned early. The third part of the thesis aims to support stream processing with latency constraints based on the incremental processing platform resulted from the second part. I perform a benchmark study to understand the sources of latency. I then propose a number of necessary architecture changes to support stream processing, and augment the platform with new latency-aware model-driven resource planning and latency-aware runtime scheduling techniques to meet user-specified latency constraints while maximizing throughput. Experiments using real-world workloads show that the techniques reduce the latency from tens or hundreds of seconds to sub-second, with 2x-5x increase in throughput. The new platform offers 1-2 orders of magnitude improvements over Storm, a commercial-grade distributed stream system, and Spark Streaming, a state-of-the-art academic prototype, when considering both latency and throughput