分布式流数据加载和查询技术优化

分布式流查询是一种基于数据流的实时查询计算方法,近年来得到了广泛的关注和快速发展。综述了分布式流处理框架在实时关系型查询上取得的研究成果;对涉及分布式数据加载、分布式流计算框架、分布式流查询的产品进行了分析和比较;提出了基于Spark Streaming和Apache Kafka构建的分布式流查询模型,以并发加载多个文件源的形式,设计内存文件系统实现数据的快速加载,相较于基于Apache Flume的加载技术提速1倍以上。在Spark Streaming的基础上,实现了基于Spark SQL的分布式流查询接口,并提出了自行编码解析SQL语句的方法,实现了分布式查询。测试结果表明,在查询语句复杂的情况下,自行编码解析SQL的查询效率具有明显的优势。     

Using grouping variables to express complex decision support queries

Performing complex analysis on top of massive data stores is essential to most modern enterprises and organizations and requires simple, flexible and powerful syntactic constructs to express naturally and succinctly complex decision support queries. In addition, these linguistic features have to be coupled by appropriate evaluation and optimization techniques in order to efficiently compute these queries. In this article we review the concept of grouping variable and describe a simple SQL extension to match it. We show that this extension enables the facile expression of a large class of practical data analysis queries. Besides syntactic simplicity, grouping variables can be neatly modeled in relational algebra via a relational operator, called MD-join. MD-join combines joins and group-bys (a frequent case in decision support queries) into one operator, allowing novel evaluation and optimization techniques. By making explicit how joins interact with group bys, we provide the optimizer with enough information to use specific algorithms and employ appropriate optimization plans, not easily detectable previously. Several experiments demonstrate substantial performance improvements, in some cases of one or two orders of magnitude. The work on grouping variables have influenced at least one commercial system and the standardization of ANSI SQL and implementations of it have been studied in the context of telecom applications, medical and bio-informatics, finance and others. Finally, current work studies the potential of grouping variables in formulating decision support queries over streams of data, one of the latest research trends in database community.     

The CQL continuous query language: semantic foundations and query execution

CQL, a continuous query language, is supported by the STREAM prototype data stream management system (DSMS) at Stanford. CQL is an expressive SQL-based declarative language for registering continuous queries against streams and stored relations. We begin by presenting an abstract semantics that relies only on “black-box” mappings among streams and relations. From these mappings we define a precise and general interpretation for continuous queries. CQL is an instantiation of our abstract semantics using SQL to map from relations to relations, window specifications derived from SQL-99 to map from streams to relations, and three new operators to map from relations to streams. Most of the CQL language is operational in the STREAM system. We present the structure of CQL's query execution plans as well as details of the most important components: operators, interoperator queues, synopses, and sharing of components among multiple operators and queries. Examples throughout the paper are drawn from the Linear Road benchmark recently proposed for DSMSs. We also curate a public repository of data stream applications that includes a wide variety of queries expressed in CQL. The relative ease of capturing these applications in CQL is one indicator that the language contains an appropriate set of constructs for data stream processing. Edited by M. Franklin     

ArchIS: an XML-based approach to transaction-time temporal database systems

Effective support for temporal applications by database systems represents an important technical objective that is difficult to achieve since it requires an integrated solution for several problems, including (i) expressive temporal representations and data models, (ii) powerful languages for temporal queries and snapshot queries, (iii) indexing, clustering and query optimization techniques for managing temporal information efficiently, and (iv) architectures that bring together the different pieces of enabling technology into a robust system. In this paper, we present the ArchIS system that achieves these objectives by supporting a temporally grouped data model on top of RDBMS. ArchIS’ architecture uses (a) XML to support temporally grouped (virtual) representations of the database history, (b) XQuery to express powerful temporal queries on such views, (c) temporal clustering and indexing techniques for managing the actual historical data in a relational database, and (d) SQL/XML for executing the queries on the XML views as equivalent queries on the relational database. The performance studies presented in the paper show that ArchIS is quite effective at storing and retrieving under complex query conditions the transaction-time history of relational databases, and can also assure excellent storage efficiency by providing compression as an option. This approach achieves full-functionality transaction-time databases without requiring temporal extensions in XML or database standards, and provides critical support to emerging application areas such as RFID.     

An annotation management system for relational databases

We present an annotation management system for relational databases. In this system, every piece of data in a relation is assumed to have zero or more annotations associated with it and annotations are propagated along, from the source to the output, as data is being transformed through a query. Such an annotation management system could be used for understanding the provenance (aka lineage) of data, who has seen or edited a piece of data or the quality of data, which are useful functionalities for applications that deal with integration of scientific and biological data. We present an extension, pSQL, of a fragment of SQL that has three different types of annotation propagation schemes, each useful for different purposes. The default scheme propagates annotations according to where data is copied from. The default-all scheme propagates annotations according to where data is copied from among all equivalent formulations of a given query. The custom scheme allows a user to specify how annotations should propagate. We present a storage scheme for the annotations and describe algorithms for translating a pSQL query under each propagation scheme into one or more SQL queries that would correctly retrieve the relevant annotations according to the specified propagation scheme. For the default-all scheme, we also show how we generate finitely many queries that can simulate the annotation propagation behavior of the set of all equivalent queries, which is possibly infinite. The algorithms are implemented and the feasibility of the system is demonstrated by a set of experiments that we have conducted.     

支持位置追溯的射频识别移动对象索引机制

随着射频通信技术的不断成熟及硬件制造成本的不断降低,射频识别(RFID)技术已开始应用于物品实时监控、跟踪与追溯领域。在供应链应用中,RFID对象数量繁多而且位置经常发生变化,如何从海量数据中查询标签对象的位置及其变化历史已成为供应链追溯亟须解决的问题。针对RFID移动对象特征及追溯查询需求,提出了一种有效的时空索引机制CR-L,并详细讨论了CR-L的结构及维护算法,包括插入、删除、二分裂及惰性分裂算法等。针对对象查询,CR-L利用读写器、时间及对象等三维信息设计了新的最小外界矩形(MBR)值计算原则,将相同读写器在相近时间内探测到的轨迹尽可能聚集于相同或相邻节点。对于轨迹查询,采用单链表将相同对象的轨迹链接起来。实验结果表明,所提索引机制具有较好的查询效率和较低的空间占用率。     

Approximation trade-offs in a Markovian stream warehouse: An empirical study

A large amount of the world's data is both sequential and low-level. Many applications need to query higher-level information (e.g., words and sentences) that is inferred from these low-level sequences (e.g., raw audio signals) using a model (e.g., a hidden Markov model). This inference process is typically statistical, resulting in high-level sequences that are imprecise. Once archived, these imprecise streams are difficult to query efficiently because of their rich semantics and large volumes, forcing applications to sacrifice either performance or accuracy. There exists little work, however, that characterizes this trade-off space and helps applications make an appropriate choice.In this paper, we study the effects – on both efficiency and accuracy – of various stream approximations such as ignoring correlations, ignoring low-probability states, or retaining only the single most likely sequence of events. Through experiments on a real-world RFID data set, we identify conditions under which various approximations can improve performance by several orders of magnitude, with only minimal effects on query results. We also identify cases when the full rich semantics are necessary. This study is the first to evaluate the cost vs. quality trade-off of imprecise stream models.We perform this study using Lahar, a prototype Markovian stream warehouse. A secondary contribution of this paper is the development of query semantics and algorithms for processing aggregation queries on the output of pattern queries—we develop these queries in order to more fully understand the effects of approximation on a wider set of imprecise stream queries.     

Processing count queries over event streams at multiple time granularities

Management and analysis of streaming data has become crucial with its applications to web, sensor data, network traffic data, and stock market. Data streams consist of mostly numeric data but what is more interesting are the events derived from the numerical data that need to be monitored. The events obtained from streaming data form event streams. Event streams have similar properties to data streams, i.e., they are seen only once in a fixed order as a continuous stream. Events appearing in the event stream have time stamps associated with them at a certain time granularity, such as second, minute, or hour. One type of frequently asked queries over event streams are count queries, i.e., the frequency of an event occurrence over time. Count queries can be answered over event streams easily, however, users may ask queries over different time granularities as well. For example, a broker may ask how many times a stock increased in the same time frame, where the time frames specified could be an hour, day, or both. Such types of queries are challenging especially in the case of event streams where only a window of an event stream is available at a certain time instead of the whole stream. In this paper, we propose a technique for predicting the frequencies of event occurrences in event streams at multiple time granularities. The proposed approximation method efficiently estimates the count of events with a high accuracy in an event stream at any time granularity by examining the distance distributions of event occurrences. The proposed method has been implemented and tested on different real data sets including daily price changes in two different stock exchange markets. The obtained results show its effectiveness.     

面向流数据的数据管理系统的研究

传统关系数据库系统通常用来存储没有时间概念的相对静止的数据, 对于一些新的应用领域, 信息是以数据序列的形式产生并且需要实时地、持续地进行处理, 这就超出了传统系统的解决能力。数据流数据管理系统是面向流数据而设计的数据管理系统, 它能有效地处理输入流数据并提供持续检索的功能。从整体上分析数据流管理系统的体系结构, 重点讨论基于流数据的数据模型和流查询。     

数据流复杂查询处理的研究

在连续的数据流上提供查询的应答对很多应用环境来说是一个极为重要的需求。本文主要探索了如何使用有限的内存在数据流上进行聚集SQL查询，以获得近似的结果。使用随机草图技术，计算非常小的数据流草图，以获得泉集查询的近似结果，并保证误差能在一定的范围之内。并讨论了．在草图方法中如何利用已有的直方图统计信息来提高应答的质量。其关键的思想就是对属性域进行智能化的划分，分解草图化问题，确保所获得查询的结果具有合适的近似精度。不论从理论还是实验上都可以证明草图提供的聚集查询结果比传统的直方图更有效、更精确。     

Bayesian Classifiers Programmed in SQL

The Bayesian classifier is a fundamental classification technique. In this work, we focus on programming Bayesian classifiers in SQL. We introduce two classifiers: Naive Bayes and a classifier based on class decomposition using K-means clustering. We consider two complementary tasks: model computation and scoring a data set. We study several layouts for tables and several indexing alternatives. We analyze how to transform equations into efficient SQL queries and introduce several query optimizations. We conduct experiments with real and synthetic data sets to evaluate classification accuracy, query optimizations, and scalability. Our Bayesian classifier is more accurate than Naive Bayes and decision trees. Distance computation is significantly accelerated with horizontal layout for tables, denormalization, and pivoting. We also compare Naive Bayes implementations in SQL and C++: SQL is about four times slower. Our Bayesian classifier in SQL achieves high classification accuracy, can efficiently analyze large data sets, and has linear scalability.     

基于Aurora系统的持续型查询语言设计与实现

随着新型数据应用的不断出现,针对流形态数据的数据流管理系统已经成为数据管理领域研究的新热点。针对目前通用数据流管理系统只支持基于操作符流图的查询表达方式这一不足,设计了一种新的持续型数据流查询语言,并在通用数据流处理系统Aurora上进行了实现。为验证新语言的表达能力,该系统使用新语言定义了数据流基准测试Linear Road Benchmark的查询集,在Aurora系统上部署运行。测试结果表明针对Linear Road Benchmark的测试用例,新语言具有较完备的语义和良好的表达能力。     

TMS-RFID: Temporal management of large-scale RFID applications

In coming years, there will be billions of RFID tags living in the world tagging almost everything for tracking and identification purposes. This phenomenon will impose a new challenge not only to the network capacity but also to the scalability of event processing of RFID applications. Since most RFID applications are time sensitive, we propose a notion of Time To Live (TTL), representing the period of time that an RFID event can legally live in an RFID data management system, to manage various temporal event patterns. TTL is critical in the “Internet of Things” for handling a tremendous amount of partial event-tracking results. Also, TTL can be used to provide prompt responses to time-critical events so that the RFID data streams can be handled timely. We divide TTL into four categories according to the general event-handling patterns. Moreover, to extract event sequence from an unordered event stream correctly and handle TTL constrained event sequence effectively, we design a new data structure, namely Double Level Sequence Instance List (DLSIList), to record intermediate stages of event sequences. On the basis of this, an RFID data management system, namely Temporal Management System over RFID data streams (TMS-RFID), has been developed. This system can be constructed as a stand-alone middleware component to manage temporal event patterns. We demonstrate the effectiveness of TMS-RFID on extracting complex temporal event patterns through a detailed performance study using a range of high-speed data streams and various queries. The results show that TMS-RFID has a very high throughput, namely 170,000–870,000 events per second for different highly complex continuous queries. Moreover, the experiments also show that the main structure to record the intermediate stages in TMS-RFID does not increase exponentially with the number of events. These results demonstrate that TMS-RFID not only supports high processing speeds, but is also highly scalable.     

Decision support queries on a tape-resident data warehouse

Data warehouses collect masses of operational data, allowing analysts to extract information by issuing decision support queries on the otherwise discarded data. In many application areas (e.g. telecommunications), the warehoused data sets are multiple terabytes in size. Parts of these data sets are stored on very large disk arrays, while the remainder is stored on tape-based tertiary storage (which is one to two orders of magnitude less expensive than on-line storage). However, the inherently sequential nature of access to tape-based tertiary storage makes the efficient access to tape-resident data difficult to accomplish through conventional databases.In this paper, we present a way to make access to a massive tape-resident data warehouse easy and efficient. Ad hoc decision support queries usually involve large scale and complex aggregation over the detail data. These queries are difficult to express in SQL, and frequently require self-joins on the detail data (which are prohibitively expensive on the disk-resident data and infeasible to compute on tape-resident data), or unnecessary multiple passes through the detail data. An extension to SQL, the extended multi feature SQL (EMF SQL) expresses complex aggregation computations in a clear manner without using self-joins. The detail data in a data warehouse usually represents a record of past activities, and therefore is temporal. We show that complex queries involving sequences can be easily expressed in EMF SQL. An EMF SQL query can be optimized to minimize the number of passes through the detail data required to evaluate the query, in many cases to only one pass. We describe an efficient query evaluation algorithm along with a query optimization algorithm that minimizes the number of passes through the detail data, and which minimizes the amount of main memory required to evaluate the query. These algorithms are useful not only in the context of tape-resident data warehouses but also in data stream systems which require similar processing techniques.     

Policy-based SQLIA detection and prevention approach for RFID systems

While SQL injection attacks have been plaguing web application systems for years, the possibility of them affecting RFID systems was only identified very recently. However, very little work exists to mitigate this serious security threat to RFID-enabled enterprise systems. In this paper, we propose a policy-based SQLIA detection and prevention method for RFID systems. The proposed technique creates data validation and sanitization policies during content analysis and enforces those policies during runtime monitoring. We tested all possible types of dynamic queries that may be generated in RFID systems with all possible types of attacks that can be mounted on those systems. We present an analysis and evaluation of the proposed approach to demonstrate the effectiveness of the proposed approach in mitigating SQLIA.     

Discovering and Exploiting Causal Dependencies for Robust Mobile Context-Aware Recommenders

Due to the resource limitation in the data stream environments, it has been reported that answering user queries according to the wavelet synopsis of a stream is an essential ability of a data stream management system (DSMS). In the literature, recent research has been elaborated upon minimizing the local error metric of an individual stream. However, many emergent applications such as stock marketing and sensor detection also call for the need of recording multiple streams in a commercial DSMS. As shown in our thorough analysis and experimental studies, minimizing global error in multiple-stream environments leads to good reliability for DSMS to answer the queries. In contrast, only minimizing local error may lead to a significant loss of query accuracy. As such, we first study in this paper the problem of maintaining the wavelet coefficients of multiple streams within collective memory so that the predetermined global error metric is minimized. Moreover, we also examine a promising application in the multistream environment, that is, the queries for top-k range sum. We resolve the problem of efficient top-k query processing with minimized global error by developing a general framework. For the purposes of maintaining the wavelet coefficients and processing top-k queries, several well- designed algorithms are utilized to optimize the performance of each primary component of this general framework. We also evaluate the proposed algorithms empirically on real and simulated data streams and show that our framework can process top-k queries accurately and efficiently.     

基于概率模型的数据流预测查询算法

挖掘在线数据流的变化趋势并预测未来时间窗口上的可能值,可以为许多时间敏感的应用提供重要决策支持.通过将数量可能无限的流数据元素映射到离散的且数量有限的流数据状态空间,不断变化的流数据变化趋势可以模拟成连续的流数据状态变化的过程,进而在很小的时间与空间代价下,数据流状态变迁的趋势动态存储在状态变迁图中.通过分析状态变迁图中的流数据变迁的统计规律,数据流上未来时刻的可能值可以应用马尔可夫模型在线连续预测.     

Answering queries using materialized views with minimum size

In this paper, we study the following problem. Given a database and a set of queries, we want to find a set of views that can compute the answers to the queries, such that the amount of space, in bytes, required to store the viewset is minimum on the given database. (We also handle problem instances where the input has a set of database instances, as described by an oracle that returns the sizes of view relations for given view definitions.) This problem is important for applications such as distributed databases, data warehousing, and data integration. We explore the decidability and complexity of the problem for workloads of conjunctive queries. We show that results differ significantly depending on whether the workload queries have self-joins. Further, for queries without self-joins we describe a very compact search space of views, which contains all views in at least one optimal viewset. We present techniques for finding a minimum-size viewset for a single query without self-joins by using the shape of the query and its constraints, and validate the approach by extensive experiments. Part of this article was published elsewhere [Chirkova, R., Li, C.: Materializing views with minimal size to answer queries. PODS (2003)]. In addition to the prior materials, this article contains new theoretical results, as well as new results on how to efficiently implement the proposed techniques (Sects. 5 and 5.4)     

Efficient Process of Top-k Range-Sum Queries over Multiple Streams with Minimized Global Error

Due to the resource limitation in the data stream environments, it has been reported that answering user queries according to the wavelet synopsis of a stream is an essential ability of a Data Stream Management System (DSMS). In the literature, recent research has been elaborated upon minimizing the local error metric of an individual stream. However, many emergent applications, such as stock marketing and sensor detection, also call for the need of recording multiple streams in a commercial DSMS. As shown in our thorough analysis and experimental studies, minimizing global error in multiple-stream environments leads to good reliability for DSMS to answer the queries; in contrast, only minimizing local error may lead to significant loss of query accuracy. As such, we first study in this paper the problem of maintaining the wavelet coefficients of multiple streams within collective memory so that the predetermined global error metric is minimized. Moreover, we also examine a promising application in the multistream environment, i.e., the queries for top-k range sum. We resolve the problem of efficient top-k query processing with minimized global error by developing a general framework. For the purposes of maintaining the wavelet coefficients and processing top-k queries, several well-designed algorithms are utilized to optimize the performance of each primary component of this general framework. We also evaluate the proposed algorithms empirically on real and simulated data streams and show that our framework can process top-k queries accurately and efficiently.     

Seamlessly integrating similarity queries in SQL

Modern database applications are increasingly employing database management systems (DBMS) to store multimedia and other complex data. To adequately support the queries required to retrieve these kinds of data, the DBMS need to answer similarity queries. However, the standard structured query language (SQL) does not provide effective support for such queries. This paper proposes an extension to SQL that seamlessly integrates syntactical constructions to express similarity predicates to the existing SQL syntax and describes the implementation of a similarity retrieval engine that allows posing similarity queries using the language extension in a relational DBMS. The engine allows the evaluation of every aspect of the proposed extension, including the data definition language and data manipulation language statements, and employs metric access methods to accelerate the queries. Copyright © 2008 John Wiley & Sons, Ltd.