End-to-End Entity Resolution for Big Data: A Survey

One of the most important tasks for improving data quality and the reliability of data analytics results is Entity Resolution (ER). ER aims to identify different descriptions that refer to the same real-world entity, and remains a challenging problem. While previous works have studied specific aspects of ER (and mostly in traditional settings), in this survey, we provide for the first time an end-to-end view of modern ER workflows, and of the novel aspects of entity indexing and matching methods in order to cope with more than one of the Big Data characteristics simultaneously. We present the basic concepts, processing steps and execution strategies that have been proposed by different communities, i.e., database, semantic Web and machine learning, in order to cope with the loose structuredness, extreme diversity, high speed and large scale of entity descriptions used by real-world applications. Finally, we provide a synthetic discussion of the existing approaches, and conclude with a detailed presentation of open research directions

Recurring Query Processing on Big Data

The advances in hardware, software, and networks have enabled applications from business enterprises, scientific and engineering disciplines, to social networks, to generate data at unprecedented volume, variety, velocity, and varsity not possible before. Innovation in these domains is thus now hindered by their ability to analyze and discover knowledge from the collected data in a timely and scalable fashion. To facilitate such large-scale big data analytics, the MapReduce computing paradigm and its open-source implementation Hadoop is one of the most popular and widely used technologies. Hadoop\u27s success as a competitor to traditional parallel database systems lies in its simplicity, ease-of-use, flexibility, automatic fault tolerance, superior scalability, and cost effectiveness due to its use of inexpensive commodity hardware that can scale petabytes of data over thousands of machines. Recurring queries, repeatedly being executed for long periods of time on rapidly evolving high-volume data, have become a bedrock component in most of these analytic applications. Efficient execution and optimization techniques must be designed to assure the responsiveness and scalability of these recurring queries. In this dissertation, we thoroughly investigate topics in the area of recurring query processing on big data. In this dissertation, we first propose a novel scalable infrastructure called Redoop that treats recurring query over big evolving data as first class citizens during query processing. This is in contrast to state-of-the-art MapReduce/Hadoop system experiencing significant challenges when faced with recurring queries including redundant computations, significant latencies, and huge application development efforts. Redoop offers innovative window-aware optimization techniques for recurring query execution including adaptive window-aware data partitioning, window-aware task scheduling, and inter-window caching mechanisms. Redoop retains the fault-tolerance of MapReduce via automatic cache recovery and task re-execution support as well. Second, we address the crucial need to accommodate hundreds or even thousands of recurring analytics queries that periodically execute over frequently updated data sets, e.g., latest stock transactions, new log files, or recent news feeds. For many applications, such recurring queries come with user-specified service-level agreements (SLAs), commonly expressed as the maximum allowed latency for producing results before their merits decay. On top of Redoop, we built a scalable multi-query sharing engine tailored for recurring workloads in the MapReduce infrastructure, called Helix. Helix deploys new sliced window-alignment techniques to create sharing opportunities among recurring queries without introducing additional I/O overheads or unnecessary data scans. Furthermore, Helix introduces a cost/benefit model for creating a sharing plan among the recurring queries, and a scheduling strategy for executing them to maximize the SLA satisfaction. Third, recurring analytics queries tend to be expensive, especially when query processing consumes data sets in the hundreds of terabytes or more. Time sensitive recurring queries, such as fraud detection, often come with tight response time constraints as query deadlines. Data sampling is a popular technique for computing approximate results with an acceptable error bound while reducing high-demand resource consumption and thus improving query turnaround times. In this dissertation, we propose the first fast approximate query engine for recurring workloads in the MapReduce infrastructure, called Faro. Faro introduces two key innovations: (1) a deadline-aware sampling strategy that builds samples from the original data with reduced sample sizes compared to uniform sampling, and (2) adaptive resource allocation strategies that maximally improve the approximate results while assuring to still meet the response time requirements specified in recurring queries. In our comprehensive experimental study of each part of this dissertation, we demonstrate the superiority of the proposed strategies over state-of-the-art techniques in scalability, effectiveness, as well as robustness

Goal-based analytic composition for on- and off-line execution at scale

Crafting scalable analytics in order to extract actionable business intelligence is a challenging endeavour, requiring multiple layers of expertise and experience. Often, this expertise is irreconcilably split between an organisation’s engineers and subject matter or domain experts. Previous approaches to this problem have relied on technically adept users with tool-specific training. These approaches have generally not targeted the levels of performance and scalability required to harness the sheer volume and velocity of large-scale data analytics. In this paper, we present a novel approach to the automated planning of scalable analytics using a semantically rich type system, the use of which requires little programming expertise from the user. This approach is the first of its kind to permit domain experts with little or no technical expertise to assemble complex and scalable analytics, for execution both on- and offline, with no lower-level engineering support. We describe in detail (i) an abstract model of analytic assembly and execution; (ii) goal-based planning and (iii) code generation using this model for both on- and off-line analytics. Our implementation of this model, MENDELEEV, is used to (iv) demonstrate the applicability of our approach through a series of case studies, in which a single interface is used to create analytics that can be run in real-time (on-line) and batch (off-line) environments. We (v) analyse the performance of the planner, and (vi) show that the performance of MENDELEEV’s generated code is comparable with that of hand-written analytics

Development of a pattern library and a decision support system for building applications in the domain of scientific workflows for e-Science

Karastoyanova et al. created eScienceSWaT (eScience SoftWare Engineering Technique), that targets at providing a user-friendly and systematic approach for creating applications for scientific experiments in the domain of e-Science. Even though eScienceSWaT is used, still many choices about the scientific experiment model, IT experiment model and infrastructure have to be made. Therefore, a collection of best practices for building scientific experiments is required. Additionally, these best practice need to be connected and organized. Finally, a Decision Support System (DSS) that is based on the best practices and enables decisions about the various choices for e-Science solutions, needs to be developed. Hence, various e-Science applications are examined in this thesis. Best practices are recognised by abstracting from the identified problem-solution pairs in the e-Science applications. Knowledge and best practices from natural science, computer science and software engineering are stored in patterns. Furthermore, relationship types among patterns are worked out. Afterwards, relationships among the patterns are defined and the patterns are organized in a pattern library. In addition, the concept for a DSS that provisions the patterns and its prototypical implementation are presented

Runtime Prediction for Scale-Out Data Analytics

Many analytics applications generate mixed workloads, i.e., workloads comprised of analytical tasks with different processing characteristics including data pre-processing, SQL, and iterative machine learning algorithms. Examples of such mixed workloads can be found in web data analysis, social media analysis, and graph analytics, where they are executed repetitively on large input datasets (e.g., "Find the average user time spent on the top 10 most popular web pages on the UK domain web graph."). Scale-out processing engines satisfy the needs of these applications by distributing the data and the processing task efficiently among multiple workers that are first reserved and then used to execute the task in parallel on a cluster of machines. Finding the resource allocation that can complete the workload execution within a given time constraint, and optimizing cluster resource allocations among multiple analytical workloads motivates the need for estimating the runtime of the workload before its actual execution. Predicting runtime of analytical workloads is a challenging problem as runtime depends on a large number of factors that are hard to model a priori execution. These factors can be summarized as workload characteristics (i.e., data statistics and processing costs), the execution configuration (i.e., deployment, resource allocation, and software settings), and the cost model that captures the interplay among all of the above parameters. While conventional cost models proposed in the context of query optimization can assess the relative order among alternative SQL query plans, they are not aimed to estimate absolute runtime. Additionally, conventional models are ill-equipped to estimate the runtime of iterative analytics that are executed repetitively until convergence and that of user defined data pre-processing operators which are not "owned" by the underlying data management system. This thesis demonstrates that runtime for data analytics can be predicted accurately by breaking the analytical tasks into multiple processing phases, collecting key input features during a reference execution on a sample of the dataset, and then using the features to build per-phase cost models. We develop prediction models for three categories of data analytics produced by social media applications: iterative machine learning, data pre-processing, and reporting SQL. The prediction framework for iterative analytics, PREDIcT, addresses the challenging problem of estimating the number of iterations, and per-iteration runtime for a class of iterative machine learning algorithms that are run repetitively until convergence. The hybrid prediction models we develop for data pre-processing tasks and for reporting SQL combine the benefits of analytical modeling with that of machine learning-based models. Through a training methodology and a pruning algorithm we reduce the cost of running training queries to a minimum while maintaining a good level of accuracy for the models

Platforms for deployment of scalable on- and off-line data analytics.

The ability to exploit the intelligence concealed in bulk data to generate actionable insights is increasingly providing competitive advantages to businesses, government agencies, and charitable organisations. The burgeoning field of Data Science, and its related applications in the field of Data Analytics, finds broader applicability with each passing year. This expansion of users and applications is matched by an explosion in tools, platforms, and techniques designed to exploit more types of data in larger volumes, with more techniques, and at higher frequencies than ever before. This diversity in platforms and tools presents a new challenge for organisations aiming to integrate Data Science into their daily operations. Designing an analytic for a particular platform necessarily involves “lock-in” to that specific implementation – there are few opportunities for algorithmic portability. It is increasingly challenging to find engineers with experience in the diverse suite of tools available as well as understanding the precise details of the domain in which they work: the semantics of the data, the nature of queries and analyses to be executed, and the interpretation and presentation of results. The work presented in this thesis addresses these challenges by introducing a number of techniques to facilitate the creation of analytics for equivalent deployment across a variety of runtime frameworks and capabilities. In the first instance, this capability is demonstrated using the first Domain Specific Language and associated runtime environments to target multiple best-in-class frameworks for data analysis from the streaming and off-line paradigms. This capability is extended with a new approach to modelling analytics based around a semantically rich type system. An analytic planner using this model is detailed, thus empowering domain experts to build their own scalable analyses, without any specific programming or distributed systems knowledge. This planning technique is used to assemble complex ensembles of hybrid analytics: automatically applying multiple frameworks in a single workflow. Finally, this thesis demonstrates a novel approach to the speculative construction, compilation, and deployment of analytic jobs based around the observation of user interactions with an analytic planning system