There is a large body of recent work applying machine learning (ML) techniques to query optimization and query performance prediction in relational database management systems (RDBMSs). However, these works typically ignore the effect of \textit{intra-parallelism} -- a key component used to boost the performance of OLAP queries in practice -- on query performance prediction. In this paper, we take a first step towards filling this gap by studying the problem of \textit{tuning the degree of parallelism (DOP) via ML techniques} in Microsoft SQL Server, a popular commercial RDBMS that allows an individual query to execute using multiple cores.
In our study, we cast the problem of DOP tuning as a {\em regression} task, and examine how several popular ML models can help with query performance prediction in a multi-core setting. We explore the design space and perform an extensive experimental study comparing different models against a list of performance metrics, testing how well they generalize in different settings: $(i)$ to queries from the same template, $(ii)$ to queries from a new template, $(iii)$ to instances of different scale, and $(iv)$ to different instances and queries. Our experimental results show that a simple featurization of the input query plan that ignores cost model estimations can accurately predict query performance, capture the speedup trend with respect to the available parallelism, as well as help with automatically choosing an optimal per-query DOP.
XML is becoming the most relevant new standard for data representation and exchange on the WWW. Novel languages for extracting and restructuring the XML content have been proposed, some in the tradition of database query languages (i.e. SQL, OQL), others more closely inspired by XML. No standard for XML query language has yet been decided, but the discussion is ongoing within the World Wide Web Consortium and within many academic institutions and Internet-related major companies. We present a comparison of five, representative query languages for XML, highlighting their common features and dif…
The vast amounts of data used in social, business or traffic networks, biology and other natural sciences are often managed in graph-based data sets, consisting of a few thousand up to billions and trillions of vertices and edges, respectively. Typical applications utilizing such data either execute one or a few complex queries or many small queries at the same time interactively or as batch jobs. Furthermore, graph processing is inherently complex, as data sets can substantially differ (scale free vs. constant degree), and algorithms exhibit diverse behavior (computational intensity, local or…
As Knowledge Graphs (KGs) continue to gain widespread momentum for use in different domains, storing the relevant KG content and efficiently executing queries over them are becoming increasingly important. A range of Data Management Systems (DMSs) have been employed to process KGs. This paper aims to provide an in-depth analysis of query performance across diverse DMSs and KG query types. Our aim is to provide a fine-grained, comparative analysis of four major DMS types, namely, row-, column-, graph-, and document-stores, against major query types, namely, subject-subject, subject-object, tree…
LARODEC Laboratory – Institut Supérieur de Gestion de Tunis（突尼斯高等管理学院LARODEC实验室）
Since formulation of Inductive Database (IDB) problem, several Data Mining (DM) languages have been proposed, confirming that KDD process could be supported via inductive queries (IQ) answering. This paper reviews the existing DM languages. We are presenting important primitives of the DM language and classifying our languages according to primitives' satisfaction. In addition, we presented languages' syntaxes and tried to apply each one to a database sample to test a set of KDD operations. This study allows us to highlight languages capabilities and limits, which is very useful for future wor…
With the continuous increase of online services as well as energy costs, energy consumption becomes a significant cost factor for the evaluation of data center operations. A significant contributor to that is the performance of database servers which are found to constitute the backbone of online services. From a software approach, while a set of novel data management technologies appear in the market e.g. key-value based or in-memory databases, classic relational database management systems (RDBMS) are still widely used. In addition from a hardware perspective, the majority of database server…
Hadoop is emerging as the primary data hub in enterprises, and SQL represents the de facto language for data analysis. This combination has led to the development of a variety of SQL-on-Hadoop systems in use today. While the various SQL-on-Hadoop systems target the same class of analytical workloads, their different architectures, design decisions and implementations impact query performance. In this work, we perform a comparative analysis of four state-of-the-art SQL-on-Hadoop systems (Impala, Drill, Spark SQL and Phoenix) using the Web Data Analytics micro benchmark and the TPC-H benchmark o…
Increasing resource demands require relational databases to scale. While relational databases are well suited for vertical scaling, specialized hardware can be expensive. Conversely, emerging NewSQL and NoSQL data stores are designed to scale horizontally. NewSQL databases provide ACID transaction support; however, joins are limited to the partition keys, resulting in restricted query expressiveness. On the other hand, NoSQL databases are designed to scale out linearly on commodity hardware; however, they are limited by slow join performance. Hence, we consider if the NoSQL join performance ca…
In this paper, we provide a comprehensive rigorous modeling for multidimensional spaces with hierarchically structured dimensions in several layers of abstractions and data cubes that live in such spaces. We model cube queries and their semantics and define typical OLAP operators like Selections, Roll-Up, Drill-Down, etc. The model serves as the basis to offer the main contribution of this paper which includes theorems and algorithms for being able to associate data cube queries via comparative operations that are evaluated only on the syntax of the queries involved. Specifically, these operat…
Traditional data systems require specialized technical skills where users need to understand the data organization and write precise queries to access data. Therefore, novice users who lack technical expertise face hurdles in perusing and analyzing data. Existing tools assist in formulating queries through keyword search, query recommendation, and query auto-completion, but still require some technical expertise. An alternative method for accessing data is Query by Example (QBE), where users express their data exploration intent simply by providing examples of their intended data. We study a s…
Most works on Web services has focused on discovery, composition and selection processes of these kinds of services. Other few works were interested in how to represent Web services search queries. However, these queries cannot be processed by ensuring a high level of performance without being adequately represented first. To this end, different query languages were designed. Even so, in the absence of a standard, these languages are quite various. Their diversity makes it difficult choosing the most suitable language. In fact, this language should be able to cover all types of preferences or …
University of Nevada（内华达大学）；University of California（加利福尼亚大学）
Federated Learning (FL) is a machine learning approach that allows multiple clients to collaboratively learn a shared model without sharing raw data. However, current FL systems provide an all-in-one solution, which can hinder the wide adoption of FL in certain domains such as scientific applications. To overcome this limitation, this paper proposes a decoupling approach that enables clients to customize FL applications with specific data subsystems. To evaluate this approach, the authors develop a framework called Data-Decoupling Federated Learning (DDFL) and compare it with state-of-the-art …
Efficient large-scale annotation of genomic intervals is essential for personal genome interpretation in the realm of precision medicine. There are 13 possible relations between two intervals according to Allen's interval algebra. Conventional interval trees are routinely used to identify the genomic intervals satisfying a coarse relation with a query interval, but cannot support efficient query for more refined relations such as all Allen's relations. We design and implement a novel approach to address this unmet need. Through rewriting Allen's interval relations, we transform an interval que…
