Non-relational database systems (NRDS), such as graph, document, key-value, and wide-column, have gained much attention in various trending (business) application domains like smart logistics, social network analysis, and medical applications, due to their data model variety and scalability. The broad data variety and sheer size of datasets pose unique challenges for the system design and runtime (incl. power consumption). While CPU performance scaling becomes increasingly more difficult, we argue that NRDS can benefit from adding field programmable gate arrays (FPGAs) as accelerators. However, FPGA-accelerated NRDS have not been systematically studied, yet.
To facilitate understanding of this emerging domain, we explore the fit of FPGA acceleration for NRDS with a focus on data model variety. We define the term NRDS class as a group of non-relational database systems supporting the same data model. This survey describes and categorizes the inherent differences and non-trivial trade-offs of relevant NRDS classes as well as their commonalities in the context of common design decisions when building such a system with FPGAs. For example, we found in the literature that for key-value stores the FPGA should be placed into the system as a smart network interface card (SmartNIC) to benefit from direct access of the FPGA to the network. However, more complex data models and processing of other classes (e.g., graph and document) commonly require more elaborate near-data or socket accelerator placements where the FPGA respectively has the only or shared access to main memory. Across the different classes, FPGAs can be used as communication layer or for acceleration of operators and data access. We close with open research and engineering challenges to outline the future of FPGA-accelerated NRDS.
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.
In this paper, we motivated the need for relational database systems to support subset query processing. We defined new operators in relational algebra, and new constructs in SQL for expressing subset queries. We also illustrated the applicability of subset queries through different examples expressed using extended SQL statements and relational algebra expressions. Our aim is to show the utility of subset queries for next generation applications.
Fuzziness in databases is used to denote uncertain or incomplete data. Relational Databases stress on the nature of the data to be certain. This certainty based data is used as the basis of the normalization approach designed for traditional relational databases. But real world data may not always be certain, thereby making it necessary to design an approach for normalization that deals with fuzzy data. This paper focuses on the approach for designing the fifth normal form (5NF) based on join dependencies for fuzzy data. The basis of join dependency for fuzzy relational databases is derived fr…
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 …
Mapping relational databases to RDF is a fundamental problem for the development of the Semantic Web. We present a solution, inspired by draft methods defined by the W3C where relational databases are directly mapped to RDF and OWL. Given a relational database schema and its integrity constraints, this direct mapping produces an OWL ontology, which, provides the basis for generating RDF instances. The semantics of this mapping is defined using Datalog. Two fundamental properties are information preservation and query preservation. We prove that our mapping satisfies both conditions, even for r…
We present a new application for keyword search within relational databases, which uses a novel algorithm to solve the join discovery problem by finding Memex-like trails through the graph of foreign key dependencies. It differs from previous efforts in the algorithms used, in the presentation mechanism and in the use of primary-key only database queries at query-time to maintain a fast response for users. We present examples using the DBLP data set.
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 …
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…
The Relational Database Aspects of Argonnes ATLAS Control System Argonnes ATLAS (Argonne Tandem Linac Accelerator System) control system comprises two separate database concepts. The first is the distributed real-time database structure provided by the commercial product Vsystem [1]. The second is a more static relational database archiving system designed by ATLAS personnel using Oracle Rdb [2] and Paradox [3] software. The configuration of the ATLAS facility has presented a unique opportunity to construct a control system relational database that is capable of storing and retrieving complete…
We study here the impact of priorities on conflict resolution in inconsistent relational databases. We extend the framework of repairs and consistent query answers. We propose a set of postulates that an extended framework should satisfy and consider two instantiations of the framework: (locally preferred) l-repairs and (globally preferred) g-repairs. We study the relationships between them and the impact each notion of repair has on the computational complexity of repair checking and consistent query answers.
Factorised databases are relational databases that use compact factorised representations at the physical layer to reduce data redundancy and boost query performance. This paper introduces FDB, an in-memory query engine for select-project-join queries on factorised databases. Key components of FDB are novel algorithms for query optimisation and evaluation that exploit the succinctness brought by data factorisation. Experiments show that for data sets with many-to-many relationships FDB can outperform relational engines by orders of magnitude.
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…
This paper, the first step to connect relational databases with systems consequence (Kent: "System Consequence" 2009), is concerned with the semantics of relational databases. It aims to to study system consequence in the logical/semantic system of relational databases. The paper, which was inspired by and which extends a recent set of papers on the theory of relational database systems (Spivak: "Functorial Data Migration" 2012), is linked with work on the Information Flow Framework (IFF) [http://suo.ieee.org/IFF/] connected with the ontology standards effort (SUO), since relational databases …
Conceptual modelling using the entity relationship (ER) model has been widely used for database design for a long period of time. However, studies indicate that creating a satisfactory relational model representation from an ER model is uncertain due to the insufficiencies both in the transformation methods used and in the relational model itself. In an effort to solve the issue the original ER notation has been modified, and accordingly, a new transformation algorithm has been developed. This paper presents the proposed transformation algorithm. Using a real world example it shows how the alg…
Keyword search in relational databases has been widely studied in recent years because it does not require users neither to master a certain structured query language nor to know the complex underlying database schemas. Most of existing methods focus on answering snapshot keyword queries in static databases. In practice, however, databases are updated frequently, and users may have long-term interests on specific topics. To deal with such a situation, it is necessary to build effective and efficient facility in a database system to support continual keyword queries. In this paper, we propose a…
The relational data model requires a theory of relations in which tuples are not only many-sorted, but can also have indexes that are not necessarily numerical. In this paper we develop such a theory and define operations on relations that are adequate for database use. The operations are similar to those of Codd's relational algebra, but differ in being based on a mathematically adequate theory of relations. The semantics of predicate calculus, being oriented toward the concept of satisfiability, is not suitable for relational databases. We develop an alternative semantics that assigns relati…
This paper describes our experience with using Grid files as the main storage organization for a relational database management system. We primarily focus on the following two aspects. (i) Strategies for implementing grid files efficiently. (ii) Methods for efficiency evaluating queries posed to a database organized using grid files.
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…
Besides reproducing tabular data properties of standalone tables, synthetic relational databases also require modeling the relationships between related tables. In this paper, we propose the Row Conditional-Tabular Generative Adversarial Network (RC-TGAN), a novel generative adversarial network (GAN) model that extends the tabular GAN to support modeling and synthesizing relational databases. The RC-TGAN models relationship information between tables by incorporating conditional data of parent rows into the design of the child table's GAN. We further extend the RC-TGAN to model the influence t…
Alloy is a lightweight modeling formalism based on relational algebra. In prior work with Fisler, Giannakopoulos, Krishnamurthi, and Yoo, we have presented a tool, Alchemy, that compiles Alloy specifications into implementations that execute against persistent databases. The foundation of Alchemy is an algorithm for rewriting relational algebra formulas into code for database transactions. In this paper we report on recent progress in improving the robustness and efficiency of this transformation.
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 address the problem of semantic querying of relational databases (RDB) modulo knowledge bases using very expressive knowledge representation formalisms, such as full first-order logic or its various fragments. We propose to use a first-order logic (FOL) reasoner for computing schematic answers to deductive queries, with the subsequent instantiation of these schematic answers using a conventional relational DBMS. In this research note, we outline the main idea of this technique -- using abstractions of databases and constrained clauses for deriving schematic answers. The proposed method can …
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…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
We propose unifying techniques from probabilistic databases and relational embedding models with the goal of performing complex queries on incomplete and uncertain data. We formalize a probabilistic database model with respect to which all queries are done. This allows us to leverage the rich literature of theory and algorithms from probabilistic databases for solving problems. While this formalization can be used with any relational embedding model, the lack of a well-defined joint probability distribution causes simple query problems to become provably hard. With this in mind, we introduce \…
Institute of Computational Intelligence Cz e stochowa University of Technology al. Armii Krajowej 36, 42-200 Cz e stochowa, Poland Email
This paper presents a novel relational database architecture aimed to visual objects classification and retrieval. The framework is based on the bag-of-features image representation model combined with the Support Vector Machine classification and is integrated in a Microsoft SQL Server database.
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…
One of the distinctive features of Information Retrieval systems comparing to Database Management systems, is that they offer better compression for posting lists, resulting in better I/O performance and thus faster query evaluation. In this paper, we introduce database representations of the index that reduce the size (and thus the disk I/Os) of the posting lists. This is not achieved by redesigning the DBMS, but by exploiting the non 1NF features that existing Object-Relational DBM systems (ORDBMS) already offer. Specifically, four different database representations are described and detaile…
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…
Most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional single-table machine learning techniques over such data not only incur a computational penalty for converting to a 'flat' form (mega-join), even the human-specified semantic information present in the relations is lost. In this paper, we present a practical, two-phase hierarchical meta-classification algorithm for relational databases with a semantic divide and conquer approach. We propose a recursive, prediction aggregation technique over heterogeneous classifiers applied on ind…
This paper is about Kripke structures that are inside a relational database and queried with a modal language. At first the modal language that is used is introduced, followed by a definition of the database and relational algebra. Based on these definitions two things are presented: a mapping from components of the modal structure to a relational database schema and instance, and a translation from queries in the modal language to relational algebra queries.
Despite their relatively low sampling factor, the freely available, randomly sampled status streams of Twitter are very useful sources of geographically embedded social network data. To statistically analyze the information Twitter provides via these streams, we have collected a year's worth of data and built a multi-terabyte relational database from it. The database is designed for fast data loading and to support a wide range of studies focusing on the statistics and geographic features of social networks, as well as on the linguistic analysis of tweets. In this paper we present the method o…
HRDBMS is a novel distributed relational database that uses a hybrid model combining the best of traditional distributed relational databases and Big Data analytics platforms such as Hive. This allows HRDBMS to leverage years worth of research regarding query optimization, while also taking advantage of the scalability of Big Data platforms. The system uses an execution framework that is tailored for relational processing, thus addressing some of the performance challenges of running SQL on top of platforms such as MapReduce and Spark. These include excessive materialization of intermediate re…
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…
This is Part II of a three article series on using databases for Finite Element Analysis (FEA). It discusses (1) db design, (2) data loading, (3) typical use cases during grid building, (4) typical use cases during simulation (get and put), (5) typical use cases during analysis (also done in Part III) and some performance measures of these cases. It argues that using a database is simpler to implement than custom data schemas, has better performance because it can use data parallelism, and better supports FEA modularity and tool evolution because database schema evolution, data independence, a…
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…
In this paper a tool called RDBNorma is proposed, that uses a novel approach to represent a relational database schema and its functional dependencies in computer memory using only one linked list and used for semi-automating the process of relational database schema normalization up to third normal form. This paper addresses all the issues of representing a relational schema along with its functional dependencies using one linked list along with the algorithms to convert a relation into second and third normal form by using above representation. We have compared performance of RDBNorma with e…
We address the problem of learning a distributed representation of entities in a relational database using a low-dimensional embedding. Low-dimensional embeddings aim to encapsulate a concise vector representation for an underlying dataset with minimum loss of information. Embeddings across entities in a relational database have been less explored due to the intricate data relations and representation complexity involved. Relational databases are an inter-weaved collection of relations that not only model relationships between entities but also record complex domain-specific quantitative and t…
Variability inherently exists in databases in various contexts which creates database variants. For example, variants of a database could have different schemas/content (database evolution problem), variants of a database could root from different sources (data integration problem), variants of a database could be deployed differently for specific application domain (deploying a database for different configurations of a software system), etc. Unfortunately, while there are specific solutions to each of the problems arising in these contexts, there is no general solution that accounts for vari…
