THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The vision of next generation 5G wireless communications lies in providing very high data rates (typically of Gbps order), extremely low latency, manifold increase in base station capacity, and significant improvement in users’ perceived quality of service (QoS), compared to current 4G LTE networks. Ever increasing proliferation of smart devices, introduction of new emerging multimedia applications, together with an exponential rise in wireless data (multimedia) demand and usage is already creating a significant burden on existing cellular networks. 5G wireless systems, with improved data rates, capacity, latency, and QoS are expected to be the panacea of most of the current cellular networks’ problems. In this survey, we make an exhaustive review of wireless evolution toward 5G networks. We first discuss the new architectural changes associated with the radio access network (RAN) design, including air interfaces, smart antennas, cloud and heterogeneous RAN. Subsequently, we make an in-depth survey of underlying novel mm-wave physical layer technologies, encompassing new channel model estimation, directional antenna design, beamforming algorithms, and massive MIMO technologies. Next, the details of MAC layer protocols and multiplexing schemes needed to efficiently support this new physical layer are discussed. We also look into the killer applications, considered as the major driving force behind 5G. In order to understand the improved user experience, we provide highlights of new QoS, QoE, and SON features associated with the 5G evolution. For alleviating the increased network energy consumption and operating expenditure, we make a detail review on energy awareness and cost efficiency. As understanding the current status of 5G implementation is important for its eventual commercialization, we also discuss relevant field trials, drive tests, and simulation experiments. Finally, we point out major existing research issues and identify possible future research directions.

Next Generation 5G Wireless Networks: A Comprehensive Survey

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A Break in the Clouds: Towards a Cloud Definition

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

Currently, domain scientists (DSs) face challenges in managing quality across multiple data analytics contexts (DACs). We identify and define quality of analytics (QoA) in dynamic and diverse environments, e.g., based on cloud computing resources for big data sources, as a composition of quality of data (data quality), performance, and cost, to name just the main factors. QoA is a complex matter and not just about quality of data or performance, which are typically considered separately when evaluating existing data analytics frameworks/algorithms. Frequently, the DS needs to utilize multiple frameworks to run different (sub)analytics, and, at the same time, the sub-analytics executed in these frameworks exchange inputs and outputs each other. In these cases, we observe different DACs, where a DAC refers to a particular situation in which the DS works with a specific framework to run a sub-analytics carried out by pipeline(s) or tasks in a pipeline. Each DAC has a set of interactions in the following categories:

Challenges in Enabling Quality of Analytics in the Cloud

Systems for big Internet of Things (IoT) data analytics are extremely complex. Different software components at different software stacks from different infrastructures and providers are involved in handling different types of data. Various types of incidents may occur during execution of such systems due to problems in software stacks, the data itself, and processing algorithms. Here incidents reflect unexpected context-specific situations that might happen within data themselves, machine learning algorithms, data analytics pipelines, and underlying big data services and computing platforms. It is important to address any incident that prevents the pipeline running correctly or producing the expected quality of analytics. In this paper, we show the need to characterize incidents for IoT data analytics in the cloud with real-world examples. We characterize incidents based on various aspects in IoT data analytics, including analytics phases, status of data, software services, and stakeholders. We introduce a meta-model for capturing knowledge about incidents.

Characterizing Incidents in Cloud-Based IoT Data Analytics

Crowdsourcing systems of the future (e.g., Social Compute Units—SCUs, collective adaptive systems) need to support complex collaborative processes, such as software development. This presupposes deploying ad-hoc assembled teams of human and machine services that actively collaborate and communicate among each other, exchanging different artifacts and jointly processing them. Major challenges in such environments (e.g., team formation, adaptability, runtime management of data-flow and collaboration patterns) can be somewhat alleviated by delegating the responsibility and the know-how needed for these duties to the participating crowd members, while indirectly controlling and stimulating them through appropriate incentive mechanisms. Existing process-centric collaboration modeling approaches (e.g., workflows) are incapable of encoding such incentive mechanisms. Therefore, in this paper we analyze different interaction aspects that incentive mechanisms cover and formulate them as requirements for future systems to support. We then propose an artifact-centric approach for modeling incentives in rich crowdsourcing environments that meets these requirements.

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Supporting Multilevel Incentive Mechanisms in Crowdsourcing Systems: An Artifact-Centric View

Recently, cloud computing technologies have been employed for large-scale machine-to-machine (M2M) systems, as they could potentially offer better solutions for managing monitoring data and analytics applications to support the needs of different consumers. However, there exist complex relationships between monitored objects, monitoring data, analysis features, and stakeholders in M2M that require efficient ways to handle these complex relationships. This paper presents techniques for linking and managing monitored objects, sustainability monitoring data and analytics applications for different stakeholders in cloud-based M2M systems. We describe a Platform-as-a-Service for sustainability governance that implements these techniques. We also illustrate our prototype based on a real-world cloud system for facility monitoring.

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M2M Platform-as-a-Service for sustainability governance

Achieving sustainability has become an important goal in the construction, refurbishment, operation and management of buildings. To this end, we need to achieve greater information exchange, especially, about practices and solutions for Energy Efficiency (EE) and the use of Renewable Energy Sources (RES) in buildings. However, in the building life-cycle, complex and disparate information sources are used by various stakeholders, thus understanding, integrating, managing and providing means for sharing such information is a challenging task. In this paper, we analyze the possibilities to capture, distill and disseminate expert know-how related to sustainable buildings, addressing the needs of the various stakeholders. A Sustainable Building Profile (SBP) is presented, which is a novel conceptual model designed to integrate information on EE and RES aspects of buildings. The SBP makes it possible to analyse the transformation of a particular building over time. Different stakeholders can use it to study various engineering, operation and maintenance problems in buildings related to energy efficiency.

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Hong-Linh Truong

Papers

Challenges in Enabling Quality of Analytics in the Cloud

Characterizing Incidents in Cloud-Based IoT Data Analytics

Supporting Multilevel Incentive Mechanisms in Crowdsourcing Systems: An Artifact-Centric View

M2M Platform-as-a-Service for sustainability governance

Information modelling for sustainable buildings