The Implications of Diverse Applications and Scalable Data Sets in Benchmarking Big Data Systems

Now we live in an era of big data, and big data applications are becoming more and more pervasive. How to benchmark data center computer systems running big data applications (in short big data systems) is a hot topic. In this paper, we focus on measuring the performance impacts of diverse applications and scalable volumes of data sets on big data systems. For four typical data analysis applications---an important class of big data applications, we find two major results through experiments: first, the data scale has a significant impact on the performance of big data systems, so we must provide scalable volumes of data sets in big data benchmarks. Second, for the four applications, even all of them use the simple algorithms, the performance trends are different with increasing data scales, and hence we must consider not only variety of data sets but also variety of applications in benchmarking big data systems.Comment: 16 pages, 3 figure

ShenZhen transportation system (SZTS): a novel big data benchmark suite

Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads, however, are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. In this paper, we propose ShenZhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as HiBench and CloudRank-D, consist of generic algorithms with synthetic inputs. We perform a cross-layer workload characterization at the microarchitecture level, the operating system (OS) level, and the job level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and we propose a methodology for identifying representative input data sets

Unsupervised learning for anomaly detection in Australian medical payment data

Fraudulent or wasteful medical insurance claims made by health care providers are costly for insurers. Typically, OECD healthcare organisations lose 3-8% of total expenditure due to fraud. As Australia’s universal public health insurer, Medicare Australia, spends approximately $A 34 billion per annum on the Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme, wasted spending of$A1–2.7 billion could be expected.However, fewer than 1% of claims to Medicare Australia are detected as fraudulent, below international benchmarks. Variation is common in medicine, and health conditions, along with their presentation and treatment, are heterogenous by nature. Increasing volumes of data and rapidly changing patterns bring challenges which require novel solutions. Machine learning and data mining are becoming commonplace in this field, but no gold standard is yet available. In this project, requirements are developed for real-world application to compliance analytics at the Australian Government Department of Health and Aged Care (DoH), covering: unsupervised learning; problem generalisation; human interpretability; context discovery; and cost prediction. Three novel methods are presented which rank providers by potentially recoverable costs. These methods used association analysis, topic modelling, and sequential pattern mining to provide interpretable, expert-editable models of typical provider claims. Anomalous providers are identified through comparison to the typical models, using metrics based on costs of excess or upgraded services. Domain knowledge is incorporated in a machine-friendly way in two of the methods through the use of the MBS as an ontology. Validation by subject-matter experts and comparison to existing techniques shows that the methods perform well. The methods are implemented in a software framework which enables rapid prototyping and quality assurance. The code is implemented at the DoH, and further applications as decision-support systems are in progress. The developed requirements will apply to future work in this fiel

Etiquetagem e rastreio de fontes de dados num Big Data Warehouse

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de InformaçãoOs avanços nas Tecnologias de Informação levam as organizações a procurar valor comercial e vantagem competitiva por meio da recolha, armazenamento, processamento e análise de dados. Os Data Warehouses surgem como uma peça fundamental no armazenamento dos dados, facilitando a sua análise sob diversas perspetivas e permitindo a extração de informação que poderá ser utilizada na tomada de decisão. A elevada disponibilidade de novas fontes de dados e os avanços que surgiram para a recolha e armazenamento dos mesmos, fazem com que seja produzida uma imensa quantidade de dados heterogéneos, gerados a taxas cada vez maiores. Adjacente a este facto surgiu o conceito de Big Data, associado ao volume, velocidade e variedade dos dados, ou seja, grandes volumes de dados com diferentes graus de complexidade, muitas vezes sem estrutura nem organização, caraterísticas estas que impossibilitam o uso de ferramentas tradicionais. Como tal, surge a necessidade de adotar o contexto de Big Data Warehouses, que naturalmente acarreta outros desafios, pois implica a adoção de novas tecnologias, assim como a adoção de novos modelos lógicos que permitem uma maior flexibilidade na gestão de dados não estruturados e desnormalizados. Por conseguinte, quando o volume de dados e a sua heterogeneidade começam a aumentar, uma vez que derivam de várias fontes que apresentam caraterísticas muito diferentes, emergem novos desafios associados ao Big Data, nomeadamente a Governança de Dados. A área de Governança de Dados abrange um grupo de subáreas, tais como Qualidade dos Dados e Gestão de Metadados, as quais oferecem um conjunto de processos para suportar a elevada complexidade inerente nos dados. À medida que o volume de dados num Big Data Warehouse começa a aumentar, os processos de negócio também aumentam, pelo que se torna necessário ter informação adicional sobre esses dados, por exemplo, que tabelas e atributos foram armazenados, quando e por quem foram criados e as diversas atualizações que sofreram. O objetivo desta dissertação é propor um sistema para a governança de um Big Data Warehouse, de modo a dar a conhecer o conteúdo do mesmo e a forma como este está a evoluir ao longo do tempo. Para tal, é proposto um sistema de catalogação de dados do Big Data Warehouse, baseado num grafo, através da etiquetagem e do rastreio de fontes de dados e posterior armazenamento dos metadados recolhidos numa base de dados. Para além de reunir as caraterísticas mais básicas dos dados, regista informações sobre políticas de acesso, profiling, a similaridade, key performance indicators e processos de negócio.Advances in Information Technologies lead organizations to search for commercial value and competitive advantage through collecting, storing, processing and analyzing data. Data Warehouses appear as a fundamental piece in data storage, facilitating data analysis from different perspectives and allowing the extraction of information that can be used in decision making. The high availability of new data sources and the advances that have been made for their collection and storage lead to the production of an enormous amount of heterogeneous data generated at increasing rates. Adjacent to this fact, the concept of Big Data appeared, associated to the volume, velocity and variety of data, that is, large volumes of data with different degrees of complexity, often without structure or organization, which makes it impossible to use traditional tools. Thus, the need arises to adopt the Big Data Warehouses context, which naturally brings other challenges, because it implies the adoption of new technologies, as well as the adoption of new logical models that allow greater flexibility in the management of unstructured and denormalized data. Therefore, when the volume of data and its heterogeneity start to increase, once they derive from several sources with very different characteristics, new challenges associated with Big Data emerge, namely Data Governance. The Data Governance domain covers a group of subdomains, such as Data Quality and Metadata Management, which provide a set of processes to support the high complexity inherent in the data. As the volume of data in a Big Data Warehouse starts to increase, the business processes also increase, meaning that it becomes important and necessary to know some additional information about these data, for example, which tables and attributes were stored, when and by whom were created and the several updates they suffered. The aim of this dissertation is to propose a governance system for the governance of a Big Data Warehouse, in order to make its content available, as well as how it is evolving over time. To this end, a graph-based Big Data Warehouse data cataloging system is proposed, by tagging and lineage of data sources and storing metadata in a database. In addition to gathering the basic characteristics of data, it records information about access policies, profiling, similarity, key performance indicators and business processes