PHENIX is one of the two large experiments at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory (BNL) and archives roughly 100TB of experimental data per year. In addition, large volumes of simulated data are produced at multiple off-site computing centers. For any file catalog to play a central role in data management it has to face problems associated with the need for distributed access and updates. To be used effectively by the hundreds of PHENIX collaborators in 12 countries the catalog must satisfy the following requirements: 1) contain up-to-date data, 2) provide fast and reliable access to the data, 3) have write permissions for the sites that store portions of data. We present an analysis of several available Relational Database Management Systems (RDBMS) to support a catalog meeting the above requirements and discuss the PHENIX experience with building and using the distributed file catalog.
Most of data on the Web are still stored in relational databases. Therefore, it is more important to make the correspondence between relational databases (RDB) and ontologies for storing the Web data. In this paper, we present an new approach to map the data stored in relational databases into the Semantic Web, we exploit simple mappings based on some specifications of the database schema, and we explain how relational databases can be used to define a mapping mechanism between relational database and OWL ontology. A framework has been developed, which migrates successfully RDB into OWL docume…
Constructing ontologies from relational databases is an active research topic in the Semantic Web domain. While conceptual mapping rules/principles of relational databases and ontology structures are being proposed, several software modules or plug-ins are being developed to enable the automatic conversion of relational databases into ontologies. However, the correlation between the resulting ontologies built automatically with plug-ins from relational databases and the database-to-ontology mapping principles has been given little attention. This study reviews and applies two Protégé plug-ins,…
We allow database user to script a parallel relational database engine with a procedural language. Procedural language code is executed as a user defined relational query operator called transducer. Transducer is tightly integrated with relation engine, including query optimizer, query executor and can be executed in parallel like other query operators. With transducer, we can efficiently execute queries that are very difficult to express in SQL. As example, we show how to run time series and graph queries, etc, within a parallel relational database.
Normalized relational databases are a common method for storing data, but pulling out usable denormalized data for consumption generally requires either direct access to the source data or creation of an appropriate view or table by a database administrator. End-users are thus limited in their ability to explore and use data that is stored in this manner. Presented here is a method for performing automated denormalization of relational databases at run-time, without requiring access to source data or ongoing intervention by a database administrator. Furthermore, this method does not require a …
There are significant benefits to serve deep learning models from relational databases. First, features extracted from databases do not need to be transferred to any decoupled deep learning systems for inferences, and thus the system management overhead can be significantly reduced. Second, in a relational database, data management along the storage hierarchy is fully integrated with query processing, and thus it can continue model serving even if the working set size exceeds the available memory. Applying model deduplication can greatly reduce the storage space, memory footprint, cache misses…
Graph analytics is becoming increasingly popular, with a deluge of new systems for graph analytics having been proposed in the past few years. These systems often start from the assumption that a new storage or query processing system is needed, in spite of graph data being often collected and stored in a relational database in the first place. In this paper, we study Vertica relational database as a platform for graph analytics. We show that vertex-centric graph analysis can be translated to SQL queries, typically involving table scans and joins, and that modern column-oriented databases are …
Flexible business processes can often be modelled more easily using a declarative rather than a procedural modelling approach. Process mining aims at automating the discovery of business process models. Existing declarative process mining approaches either suffer performance issues with real-life event logs or limit their expressiveness to a specific set of constaint types. Lately, with RelationalXES a relational database architecture for storing event log data has been introduced. In this technical report, we introduce a mining approach that directly works on relational event data by querying…
The execution logs that are used for process mining in practice are often obtained by querying an operational database and storing the result in a flat file. Consequently, the data processing power of the database system cannot be used anymore for this information, leading to constrained flexibility in the definition of mining patterns and limited execution performance in mining large logs. Enabling process mining directly on a database - instead of via intermediate storage in a flat file - therefore provides additional flexibility and efficiency. To help facilitate this ideal of in-database p…
During the last two decades, it has been increasingly acknowledged that the engineering of information systems usually requires a huge effort in integrating master data and business processes. This has led to a plethora of proposals, both from academia and the industry. However, such approaches typically come with ad-hoc abstractions to represent and interact with the data component. This has a twofold disadvantage. On the one hand, they cannot be used to effortlessly enrich an existing relational database with dynamics. On the other hand, they generally do not allow for integrated modelling, …
It is increasingly common to find complex data represented through the graph model. Contrary to relational models, graphs offer a high capacity for executing analytical tasks on complex data. Since a huge amount of data is still presented in terms of relational tables, it is necessary to understand how to translate this data into graphs. This paper proposes a complete mapping process that allows transforming any relational database (schema and instance) into a property graph database (schema and instance). Contrary to existing mappings, our solution preserves the three fundamental mapping prop…
Process-Aware Information System (PAIS) are IT systems that manages, supports business processes and generate large event logs from execution of business processes. An event log is represented as a tuple of the form CaseID, TimeStamp, Activity and Actor. Process Mining is an emerging area of research that deals with the study and analysis of business processes based on event logs. Process Mining aims at analyzing event logs and discover business process models, enhance them or check for conformance with an a priori model. The large volume of event logs generated are stored in databases. Relati…
The integrated management of business processes and mas- ter data is being increasingly considered as a fundamental problem, by both the academia and the industry. In this position paper, we focus on the foundations of the problem, arguing that contemporary approaches struggle to find a suitable equilibrium between data- and process-related aspects. We then propose db-nets, a new formal model that balances such two pillars through the marriage of colored Petri nets and relational databases. We invite the research community to build on this model, discussing its potential in modeling, formal ve…
The plethora of graphs and relational data give rise to many interesting graph-relational queries in various domains, e.g., finding related proteins satisfying relational predicates in a biological network. The maturity of RDBMSs motivated academia and industry to invest efforts in leveraging RDBMSs for graph processing, where efficiency is proven for vital graph queries. However, none of these efforts process graphs natively inside the RDBMS, which is particularly challenging due to the impedance mismatch between the relational and the graph models. In this paper, we propose to treat graphs a…
We present a multi-process application called HISTEX (HISTory EXerciser), which executes input histories in a generic transactional notation on commercial DBMS platforms. HISTEX could be used to discover potential errors in the implementation of Isolation Levels by Relational Database Management Systems or cases where a system behaves over restrictively. It can also be used for performance measurements related to database workloads executing on real database systems instead of simulated environments. HISTEX has been implemented in C by utilizing Embedded SQL. However, many of its ideas could b…
We present a survey of existing approaches to relational division in rank-aware databases, discuss issues of the present approaches, and outline generalizations of several types of classic division-like operations. We work in a model which generalizes the Codd model of data by considering tuples in relations annotated by ranks, indicating degrees to which tuples in relations match queries. The approach utilizes complete residuated lattices as the basic structures of degrees. We argue that unlike the classic model, relational divisions are fundamental operations which cannot in general be expre…
This write-up is the suggested lecture notes for a second level course on advanced topics in database systems for master's students of Computer Science with a theoretical focus. A prerequisite in algorithms and an exposure to database systems are required. Additional reading may require exposure to mathematical logic. The starting point for these notes are from M.Y.Vardi's survey listed herein as a reference - some of the proofs are presented as such . This select rewrite on functional dependency is intended to provide a few clarifications even though radically new design approaches are now be…
In this paper, building on work done on measuring inconsistency in knowledge bases, we introduce inconsistency measures for databases. In particular, focusing on databases with denial constraints, we first consider the natural approach of virtually transforming a database into a propositional knowledge base and then applying well-known measures. However, using this method, tuples and constraints are equally considered in charge of inconsistencies. Then, we introduce a version of inconsistency measures blaming database tuples only, i.e., treating integrity constraints as irrefutable statements.…
This tutorial overviews the state of the art in learning models over relational databases and makes the case for a first-principles approach that exploits recent developments in database research. The input to learning classification and regression models is a training dataset defined by feature extraction queries over relational databases. The mainstream approach to learning over relational data is to materialize the training dataset, export it out of the database, and then learn over it using a statistical package. This approach can be expensive as it requires the materialization of the trai…
Although researchers have devoted considerable attention to helping database users formulate queries, many users still find it challenging to specify queries that involve joining tables. To help users construct join queries for exploring relational databases, we propose ETable, a novel presentation data model that provides users with a presentation-level interactive view. This view compactly presents one-to-many and many-to-many relationships within a single enriched table by allowing a cell to contain a set of entity references. Users can directly interact with this enriched table to incremen…
The digital transformation of companies has led to the evolution of databases towards Big Data. Our work is part of this context and concerns more particularly the mechanisms to extract datasets stored in a Data Lake and to store the data in a Data Warehouse. The latter will allow, in a second time, decisional analysis. In this paper, we present the extraction mechanism limited to relational databases. To automate this process, we used the Model Driven Architecture (MDA), which offers a formalized environment for schema transformation. From the physical schemas describing a Data Lake, we propo…
Multi-objective optimization is the problem of optimizing simultaneously multiple objective functions and several techniques exist to deal with this problem. This paper aims to present the main methods that can be used to solve this issue in the context of relational databases. In particular, this work examines Top-k query to get the k best result from a dataset and Skyline query that provides a more general overview of the best results. We also discuss Flexible-skyline, a new method designed to improve upon the previous techniques, mitigating their shortcomings. For each method, we describe t…
Over the past decade, we have witnessed a dramatic evolution in main-memory capacity and multi-core parallelism of server hardware. To leverage this hardware potential, multi-core in-memory OLTP database systems have been extensively re-designed. The core objective of this re-design has been scaling up sequential execution of OLTP transactions, wherein alternative database architectures have been explored to eliminate system bottlenecks and overheads impeding inter-transaction parallelism to fully manifest. However, intra-transaction parallelism has been largely ignored by this previous work. …
Data analysis often involves comparing subsets of data across many dimensions for finding unusual trends and patterns. While the comparison between subsets of data can be expressed using SQL, they tend to be complex to write, and suffer from poor performance over large and high-dimensional datasets. In this paper, we propose a new logical operator COMPARE for relational databases that concisely captures the enumeration and comparison between subsets of data and greatly simplifies the expressing of a large class of comparative queries. We extend the database engine with optimization techniques …
One of the first attempts to set a solid theoretical foundation for extending the content of relational databases with incomplete information was the fuzzy relational model by Buckles and Petry. This structure was based on two generalizations of the traditional relational model: (1) A tuple component can be any subset of the corresponding domain, rather than a single element and (2) A similarity relation is defined on each domain. This relation satisfies the properties of reflexivity, symmetry and max-min transitivity, thus having the equality relation as a special case. This generalization ke…
Graph traversals are a basic but fundamental ingredient for a variety of graph algorithms and graph-oriented queries. To achieve the best possible query performance, they need to be implemented at the core of a database management system that aims at storing, manipulating, and querying graph data. Increasingly, modern business applications demand native graph query and processing capabilities for enterprise-critical operations on data stored in relational database management systems. In this paper we propose an extensible graph traversal framework (GRAPHITE) as a central graph processing compo…
The class of queries for detecting path is an important as those can extract implicit binary relations over the nodes of input graphs. Most of the path querying languages used by the RDF community, like property paths in W3C SPARQL 1.1 and nested regular expressions in nSPARQL are based on the regular expressions. Federated queries allow for combining graph patterns and relational database that enables the evaluations over several heterogeneous data resources within a single query. Federated queries in W3C SPARQL 1.1 currently evaluated over different SPARQL endpoints. In this paper, we presen…
Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually applying techniques to discover, control and predict the business process. Moreover, current process mining techniques assume a single case notion. However, in reallife processes often different case notions are intertwined. For example, events of the same order handling process may refer to customers, orders, order lines, deliveries, and payments. Therefore, we propose to use Multiple Viewpoint (MVP) models that re…
A significant category of NoSQL approaches is known as graph da-tabases. They are usually represented by one property graph. We introduce a functional approach to modelling relations and property graphs. Single-valued and multivalued functions will be sufficient in this case. Then, a typed λ-calculus, i.e., the language of lambda terms, will be used as a data manipulation lan-guage. Some integration options at the query language level are discussed.
Financial transactions, internet search, and data analysis are all placing increasing demands on databases. SQL, NoSQL, and NewSQL databases have been developed to meet these demands and each offers unique benefits. SQL, NoSQL, and NewSQL databases also rely on different underlying mathematical models. Polystores seek to provide a mechanism to allow applications to transparently achieve the benefits of diverse databases while insulating applications from the details of these databases. Integrating the underlying mathematics of these diverse databases can be an important enabler for polystores …
Data science teams often collaboratively analyze datasets, generating dataset versions at each stage of iterative exploration and analysis. There is a pressing need for a system that can support dataset versioning, enabling such teams to efficiently store, track, and query across dataset versions. We introduce OrpheusDB, a dataset version control system that "bolts on" versioning capabilities to a traditional relational database system, thereby gaining the analytics capabilities of the database "for free". We develop and evaluate multiple data models for representing versioned data, as well as…
这篇文章是将关系数据库与系统结果连接起来的第一步（Kent：“System Consequence”2009），关注关系数据库的语义。目的是研究关系数据库逻辑/语义系统中的系统推论。这篇论文的灵感来自于最近的一组关于关系数据库系统理论的论文（Spivak：“Functional Data Migration”2012），并扩展了这些论文，这篇论文与关于信息流框架（IFF）[http：//suo.ieee.org/IFF/]的工作相关联，该工作与本体标准工作（SUO）相关联，因为关系数据库自然地嵌入到一阶逻辑中。这里讨论的数据库语义与数据库体系结构的概念级有关。我们提供直观和技术讨论。对应于主键和外键的概念，关系数据库语义有两种形式：实体与关系相区别的区别形式，以及关系和实体一致的统一形式。区别形式对应于（Spivak：“Simplicial databases”2009）[arXiv：0904.2012]中提出的理论。统一形式是区分形式的特殊情况，对应于（Spivak：“Functional Data Migration”2012）中提出的理论。后面的文章将讨论关系数据库的各种形式，如关系代数和一阶逻辑，并将完成对关系数据库逻辑环境的描述。
A distributed graph database architecture that co-exists with the distributed relational DB for I/O and at-scale OLAP expression support with hundreds of PostGIS compatible geometry functions will be discussed in this article. The uniqueness of this implementation stems mainly from its double link topology structure for its fixed storage characteristics independent from the variance in node-to-edge connections. Another note-worthy contribution of this implementation is its non-blocking client-server communication architecture among its distributed graph servers. A non-bottlenecking partitionin…
Traditional relational databases contain a lot of latent semantic information that have largely remained untapped due to the difficulty involved in automatically extracting such information. Recent works have proposed unsupervised machine learning approaches to extract such hidden information by textifying the database columns and then projecting the text tokens onto a fixed dimensional semantic vector space. However, in certain databases, task-specific class labels may be available, which unsupervised approaches are unable to lever in a principled manner. Also, when embeddings are generated a…
In this paper, we address the problem of learning low dimension representation of entities on relational databases consisting of multiple tables. Embeddings help to capture semantics encoded in the database and can be used in a variety of settings like auto-completion of tables, fully-neural query processing of relational joins queries, seamlessly handling missing values, and more. Current work is restricted to working with just single table, or using pretrained embeddings over an external corpus making them unsuitable for use in real-world databases. In this work, we look into ways of using t…
