MapReduce is Good Enough? If All You Have is a Hammer, Throw Away Everything That's Not a Nail!

Hadoop is currently the large-scale data analysis "hammer" of choice, but there exist classes of algorithms that aren't "nails", in the sense that they are not particularly amenable to the MapReduce programming model. To address this, researchers have proposed MapReduce extensions or alternative programming models in which these algorithms can be elegantly expressed. This essay espouses a very different position: that MapReduce is "good enough", and that instead of trying to invent screwdrivers, we should simply get rid of everything that's not a nail. To be more specific, much discussion in the literature surrounds the fact that iterative algorithms are a poor fit for MapReduce: the simple solution is to find alternative non-iterative algorithms that solve the same problem. This essay captures my personal experiences as an academic researcher as well as a software engineer in a "real-world" production analytics environment. From this combined perspective I reflect on the current state and future of "big data" research

Decision Tables: Scalable Classification Exploring RDBMS Capabilities

In this paper, we report our success in building efficient scalable classifiers in the form of decision tables by exploring capabilities of modern relational database management systems. In addition to high classification accuracy, the unique features of the approach include its high training speed, linear scalability, and simplicity in implementation. More importantly, the major computation required in the approach can be implemented using standard functions provided by the modern relational DBMS. This not only makes implementation of the classifier extremely easy, further performance improvement is also expected when better processing strategies for those computations are developed and implemented in RDBMS. The novel classification approach based on grouping and counting and its implementation on top of RDBMS is described. The result

Transparent Forecasting Strategies in Database Management Systems

Whereas traditional data warehouse systems assume that data is complete or has been carefully preprocessed, increasingly more data is imprecise, incomplete, and inconsistent. This is especially true in the context of big data, where massive amount of data arrives continuously in real-time from vast data sources. Nevertheless, modern data analysis involves sophisticated statistical algorithm that go well beyond traditional BI and, additionally, is increasingly performed by non-expert users. Both trends require transparent data mining techniques that efficiently handle missing data and present a complete view of the database to the user. Time series forecasting estimates future, not yet available, data of a time series and represents one way of dealing with missing data. Moreover, it enables queries that retrieve a view of the database at any point in time - past, present, and future. This article presents an overview of forecasting techniques in database management systems. After discussing possible application areas for time series forecasting, we give a short mathematical background of the main forecasting concepts. We then outline various general strategies of integrating time series forecasting inside a database and discuss some individual techniques from the database community. We conclude this article by introducing a novel forecasting-enabled database management architecture that natively and transparently integrates forecast models

Manufacturing as a Data-Driven Practice: Methodologies, Technologies, and Tools

n recent years, the introduction and exploitation of innovative information technologies in industrial contexts have led to the continuous growth of digital shop floor envi- ronments. The new Industry-4.0 model allows smart factories to become very advanced IT industries, generating an ever- increasing amount of valuable data. As a consequence, the neces- sity of powerful and reliable software architectures is becoming prominent along with data-driven methodologies to extract useful and hidden knowledge supporting the decision making process. This paper discusses the latest software technologies needed to collect, manage and elaborate all data generated through innovative IoT architectures deployed over the production line, with the aim of extracting useful knowledge for the orchestration of high-level control services that can generate added business value. This survey covers the entire data life-cycle in manufacturing environments, discussing key functional and methodological aspects along with a rich and properly classified set of technologies and tools, useful to add intelligence to data-driven services. Therefore, it serves both as a first guided step towards the rich landscape of literature for readers approaching this field, and as a global yet detailed overview of the current state-of-the-art in the Industry 4.0 domain for experts. As a case study, we discuss in detail the deployment of the proposed solutions for two research project demonstrators, showing their ability to mitigate manufacturing line interruptions and reduce the corresponding impacts and costs

The Design and Implementation of Low-Latency Prediction Serving Systems

Machine learning is being deployed in a growing number of applications which demand real- time, accurate, and cost-efficient predictions under heavy query load. These applications employ a variety of machine learning frameworks and models, often composing several models within the same application. However, most machine learning frameworks and systems are optimized for model training and not deployment.In this thesis, I discuss three prediction serving systems designed to meet the needs of modern interactive machine learning applications. The key idea in this work is to utilize a decoupled, layered design that interposes systems on top of training frameworks to build low-latency, scalable serving systems. Velox introduced this decoupled architecture to enable fast online learning and model personalization in response to feedback. Clipper generalized this system architecture to be framework-agnostic and introduced a set of optimizations to reduce and bound prediction latency and improve prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. And InferLine provisions and manages the individual stages of prediction pipelines to minimize cost while meeting end-to-end tail latency constraints

Query-Time Data Integration

Today, data is collected in ever increasing scale and variety, opening up enormous potential for new insights and data-centric products. However, in many cases the volume and heterogeneity of new data sources precludes up-front integration using traditional ETL processes and data warehouses. In some cases, it is even unclear if and in what context the collected data will be utilized. Therefore, there is a need for agile methods that defer the effort of integration until the usage context is established. This thesis introduces Query-Time Data Integration as an alternative concept to traditional up-front integration. It aims at enabling users to issue ad-hoc queries on their own data as if all potential other data sources were already integrated, without declaring specific sources and mappings to use. Automated data search and integration methods are then coupled directly with query processing on the available data. The ambiguity and uncertainty introduced through fully automated retrieval and mapping methods is compensated by answering those queries with ranked lists of alternative results. Each result is then based on different data sources or query interpretations, allowing users to pick the result most suitable to their information need. To this end, this thesis makes three main contributions. Firstly, we introduce a novel method for Top-k Entity Augmentation, which is able to construct a top-k list of consistent integration results from a large corpus of heterogeneous data sources. It improves on the state-of-the-art by producing a set of individually consistent, but mutually diverse, set of alternative solutions, while minimizing the number of data sources used. Secondly, based on this novel augmentation method, we introduce the DrillBeyond system, which is able to process Open World SQL queries, i.e., queries referencing arbitrary attributes not defined in the queried database. The original database is then augmented at query time with Web data sources providing those attributes. Its hybrid augmentation/relational query processing enables the use of ad-hoc data search and integration in data analysis queries, and improves both performance and quality when compared to using separate systems for the two tasks. Finally, we studied the management of large-scale dataset corpora such as data lakes or Open Data platforms, which are used as data sources for our augmentation methods. We introduce Publish-time Data Integration as a new technique for data curation systems managing such corpora, which aims at improving the individual reusability of datasets without requiring up-front global integration. This is achieved by automatically generating metadata and format recommendations, allowing publishers to enhance their datasets with minimal effort. Collectively, these three contributions are the foundation of a Query-time Data Integration architecture, that enables ad-hoc data search and integration queries over large heterogeneous dataset collections

Distributed multi-label learning on Apache Spark

This thesis proposes a series of multi-label learning algorithms for classification and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes the instances across multiple nodes, and an approximate local sensitive hashing method that builds multiple hash tables to index the data. The results indicated that the predictions of the tree-based method are on par with those of an exact method while reducing the execution times in all the scenarios. The aforementioned method is then used to evaluate the quality of a selected feature subset. The optimal adaptation for a multi-label feature selection criterion is discussed and two distributed feature selection methods for multi-label problems are proposed: a method that selects the feature subset that maximizes the Euclidean norm of individual information measures, and a method that selects the subset of features maximizing the geometric mean. The results indicate that each method excels in different scenarios depending on type of features and the number of labels. Rigorous experimental studies and statistical analyses over many multi-label metrics and datasets confirm that the proposals achieve better performances and provide better scalability to bigger data than the methods compared in the state of the art

Enhanced Living Environments

This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area