In his seminal book, Shewhart (1931) makes no demand on the distribution of the characteristic to be plotted on a control chart. How then can we explain the idea that normality is, if not required, at least highly desirable? I believe that it has come about through the many statistical studies of control-chart behavior. If one is to study how a control chart behaves, it is necessary to relate it to some distribution. The obvious choice is the normal distribution because of its ubiquity as a satisfactory model. This is bolstered by the existence of the Central Limit Theorem.

Sampling: Design and Analysis

Growing main memory capacity has fueled the development of in-memory big data management and processing. By eliminating disk I/O bottleneck, it is now possible to support interactive data analytics. However, in-memory systems are much more sensitive to other sources of overhead that do not matter in traditional I/O-bounded disk-based systems. Some issues such as fault-tolerance and consistency are also more challenging to handle in in-memory environment. We are witnessing a revolution in the design of database systems that exploits main memory as its data storage layer. Many of these researches have focused along several dimensions: modern CPU and memory hierarchy utilization, time/space efficiency, parallelism, and concurrency control. In this survey, we aim to provide a thorough review of a wide range of in-memory data management and processing proposals and systems, including both data storage systems and data processing frameworks. We also give a comprehensive presentation of important technology in memory management, and some key factors that need to be considered in order to achieve efficient in-memory data management and processing.

/pdf/in-memory-big-data-management-and-processing-a-survey-1k2b0h2jut.pdf

In-Memory Big Data Management and Processing: A Survey

To the Editor.— The editorial by Samuel Vaisrub, MD, "To the Best of Our Knowledge, Which Is Limited at Best" (241:278, 1979), was delightful, and its point was unhappily correct. It brought to my mind that demolisher of analytical studies, "to the best of our knowledge," or "as far as we know." It goes like this: We treated so many people, and we know this many are well, and so many have died; and this many,from whom we have not heard, are alive and well,as far as we know. Someone from whom you have not heard is more likely to be dead than alive.

To the Best of Our Knowledge

https://hal.inria.fr/hal-01653842/document

Distributed data stream processing and edge computing

Many Big Data applications in science and industry have arisen, that require large amounts of streamed or event data to be analyzed with low latency. This paper presents a reactive strategy to enforce latency guarantees in data flows running on scalable Stream Processing Engines (SPEs), while minimizing resource consumption. We introduce a model for estimating the latency of a data flow, when the degrees of parallelism of the tasks within are changed. We describe how to continuously measure the necessary performance metrics for the model, and how it can be used to enforce latency guarantees, by determining appropriate scaling actions at runtime. Therefore, it leverages the elasticity inherent to common cloud technology and cluster resource management systems. We have implemented our strategy as part of the Nephele SPE. To showcase the effectiveness of our approach, we provide an experimental evaluation on a large commodity cluster, using both a synthetic workload as well as an application performing real-time sentiment analysis on real-world social media data.

Elastic Stream Processing with Latency Guarantees

With the SAP HANA database, SAP offers a high-performance in-memory hybrid-store database. Hybrid-store databases---that is, databases supporting row- and column-oriented data management---are getting more and more prominent. While the columnar management offers high-performance capabilities for analyzing large quantities of data, the row-oriented store can handle transactional point queries as well as inserts and updates more efficiently. To effectively take advantage of both stores at the same time the novel question whether to store the given data row- or column-oriented arises. We tackle this problem with a storage advisor tool that supports database administrators at this decision. Our proposed storage advisor recommends the optimal store based on data and query characteristics; its core is a cost model to estimate and compare query execution times for the different stores. Besides a per-table decision, our tool also considers to horizontally and vertically partition the data and manage the partitions on different stores. We evaluated the storage advisor for the use in the SAP HANA database; we show the recommendation quality as well as the benefit of having the data in the optimal store with respect to increased query performance.

/pdf/a-storage-advisor-for-hybrid-store-databases-1yoi7l2cgz.pdf

A storage advisor for hybrid-store databases

One fundamental challenge in data stream processing is to cope with the ubiquity of disorder of tuples within a stream caused by network latency, operator parallelization, merging of asynchronous streams, etc High result accuracy and low result latency are two conflicting goals in out-of-order stream processing Different applications may prefer different extent of trade-offs between the two goals However, existing disorder handling solutions either try to meet one goal to the extreme by sacrificing the other, or try to meet both goals but have shortcomings including unguaranteed result accuracy or increased complexity in operator implementation and application logic To meet different application requirements on the latency versus result accuracy trade-off in out-of-order stream processing, in this paper, we propose to make this trade-off user-configurable Particularly, focusing on sliding window aggregates, we introduce AQ-K-slack, a buffer-based quality-driven disorder handling approach AQ-K-slack leverages techniques from the fields of sampling-based approximate query processing and control theory It can adjust the input buffer size dynamically to minimize the result latency, while respecting user-specified threshold on relative errors in produced query results AQ-K-slack requires no a priori knowledge of disorder characteristics of data streams, and imposes no changes to the query operator implementation or the application logic Experiments over real-world out-of-order data streams show that, compared to the state-of-art, AQ-K-slack can reduce the average buffer size, thus the average result latency, by at least 51% while respecting user-specified requirement on the accuracy of query results

Quality-driven processing of sliding window aggregates over out-of-order data streams

A Storage Advisor for Hybrid-Store Databases

State-of-the-art visual data analysis tools ignore bandwidth limitations. They fetch millions of records of high-volume time series data from an underlying RDBMS to eventually draw only a few thousand pixels on the screen.In this work, we demonstrate a pixel-aware big data visualization system that dynamically adapts the number of data points transmitted and thus the data rate, while preserving pixel-perfect visualizations. We show how to carefully select the data points to fetch for each pixel of a visualization, using a visualization-driven data aggregation that models the visualization process. Defining all required data reduction operators at the query level, our system trades off a few milliseconds of query execution time for dozens of seconds of data transfer time. The results are significantly reduced response times and a near real-time visualization of millions of data points.Using our pixel-aware system, the audience will be able to enjoy the speed and ease of big data visualizations and learn about the scientific background of our system through an interactive evaluation component, allowing the visitor to measure, visualize, and compare competing visualization-related data reduction techniques.

/pdf/faster-visual-analytics-through-pixel-perfect-aggregation-3hcrv4kt7i.pdf

Faster visual analytics through pixel-perfect aggregation

Sliding window join is one of the most important operators for stream applications. To produce high quality join results, a stream processing system must deal with the ubiquitous disorder within input streams which is caused by network delay, parallel processing, etc. Disorder handling involves an inevitable tradeoff between the latency and the quality of produced join results. To meet different requirements of stream applications, it is desirable to provide a user-configurable result-latency vs. result-quality tradeoff. Existing disorder handling approaches either do not provide such configurability, or support only user-specified latency constraints. In this work, we advocate the idea of quality-driven disorder handling, and propose a buffer-based disorder handling approach for sliding window joins, which minimizes sizes of input-sorting buffers, thus the result latency, while respecting user-specified result-quality requirements. The core of our approach is an analytical model which directly captures the relationship between sizes of input buffers and the produced result quality. Our approach is generic. It supports m-way sliding window joins with arbitrary join conditions. Experiments on real-world and synthetic datasets show that, compared to the state of the art, our approach can reduce the result latency incurred by disorder handling by up to 95% while providing the same level of result quality.

Quality-driven disorder handling for m-way sliding window stream joins

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