StreamAPAS: Query Language and Data Model

A wide range of database applications manage time-varying data. Many temporal query languages have been proposed, each one the result of many carefully made yet subtly interacting design decisions. In this article we advocate a different approach to articulating a set of requirements, or desiderata, that directly imply the syntactic structure and core semantics of a temporal extension of an (arbitrary) nontemporal query language. These desiderata facilitate transitioning applications from a nontemporal query language and data model, which has received only scant attention thus far. The paper then introduces the notion of statement modifiers that provide a means of systematically adding temporal support to an existing query language. Statement modifiers apply to all query language statements, for example, queries, cursor definitions, integrity constraints, assertions, views, and data manipulation statements. We also provide a way to systematically add temporal support to an existing implementation. The result is a temporal query language syntax, semantics, and implementation that derives from first principles. We exemplify this approach by extending SQL-92 with statement modifiers. This extended language, termed ATSQL, is formally defined via a denotational-semantics-style mapping of temporal statements to expressions using a combination of temporal and conventional relational algebraic operators.

Most data stream management systems are based on extensions of the relational data model and query languages, but rigorous analyses of the problems and limitations of this approach, and how to overcome them, are still wanting. In this article, we elucidate the interaction between stream-oriented extensions of the relational model and continuous query language constructs, and show that the resulting expressive power problems are even more serious for data streams than for databases. In particular, we study the loss of expressive power caused by the loss of blocking query operators, and characterize nonblocking queries as monotonic functions on the database. Thus we introduce the notion of NB -completeness to assure that a query language is as suitable for continuous queries as it is for traditional database queries. We show that neither RA nor SQL are NB -complete on unordered sets of tuples, and the problem is even more serious when the data model is extended to support order—a sine-qua-non in data stream applications. The new limitations of SQL, compounded with well-known problems in applications such as sequence queries and data mining, motivate our proposal of extending the language with user-defined aggregates (UDAs). These can be natively coded in SQL, according to simple syntactic rules that set nonblocking aggregates apart from blocking ones. We first prove that SQL with UDAs is Turing complete. We then prove that SQL with monotonic UDAs and union operators can express all monotonic set functions computable by a Turing machine ( NB -completeness) and finally extend this result to queries on sequences ordered by their timestamps. The proposed approach supports data stream models that are more sophisticated than append-only relations, along with data mining queries, and other complex applications.

Spatial big data architecture: From Data Warehouses and Data Lakes to the LakeHouse

Analyzing conceptual models such as process models, data models, or organizational charts is useful for several purposes in information systems engineering (e.g., for business process improvement, compliance management, model driven software development, and software alignment). To analyze conceptual models structurally and semantically, so-called model query languages have been put forth. Model query languages take a model pattern and conceptual models as input and return all subsections of the models that match this pattern. Existing model query languages typically focus on a single modeling language and/or application area (such as analysis of execution semantics of process models), are restricted in their expressive power of representing model structures, and/or abstain from graphical pattern specification. Because these restrictions may hamper query languages from propagating into practice, we close this gap by proposing a modeling language-spanning structural model query language based on flexible graph search that, hence, provides high structural expressive power. To address ease-of-use, it allows one to specify model queries using a diagram. In this paper, we present the syntax and the semantics of the diagramed model query language (DMQL), a corresponding search algorithm, an implementation as a modeling tool prototype, and a performance evaluation.

In recent years, applications aimed at exploring and analyzing spatial data have emerged, powered by the increasing need of software that integrates Geographic Information Systems (GIS) and On-Line Analytical Processing (OLAP). These applications have been called SOLAP (Spatial OLAP). In previous work, the authors have introduced Piet, a system based on a formal data model that integrates in a single framework GIS, OLAP (On-Line Analytical Processing), and Moving Object data. Real-world problems are inherently spatio-temporal. Thus, in this paper we present a data model that extends Piet, allowing tracking the history of spatial data in the GIS layers. We present a formal study of the two typical ways of introducing time into Piet: timestamping the thematic layers in the GIS, and timestamping the spatial objects in each layer. We denote these strategies snapshot-based and timestamp-based representations, respectively, following well-known terminology borrowed from temporal databases. We present and discuss the formal model for both alternatives. Based on the timestamp-based representation, we introduce a formal First-Order spatio-temporal query language, which we denote $\mathcal{L}_t,$ able to express spatio-temporal queries over GIS, OLAP, and trajectory data. Finally, we discuss implementation issues, the update operators that must be supported by the model, and sketch a temporal extension to Piet-QL, the SQL-like query language that supports Piet.

This paper presents DEDALE, a spatial database system intended to overcome some limitations of current systems by providing an abstract and non-specialized data model and query language for the representation and manipulation of spatial objects. DEDALE relies on a logical model based on linear constraints, which generalizes the constraint database model of [KKR90]. While in the classical constraint model, spatial data is always decomposed into its convex components, in DEDALE holes are allowed to fit the need of practical applications. The logical representation of spatial data although slightly more costly in memory, has the advantage of simplifying the algorithms. DEDALE relies on nested relations, in which all sorts of data (thematic, spatial, etc.) are stored in a uniform fashion. This new data model supports declarative query languages, which allow an intuitive and efficient manipulation of spatial objects. Their formal foundation constitutes a basis for practical query optimization. We describe several evaluation rules tailored for geometric data and give the specification of an optimizer module for spatial queries. Except for the latter module, the system has been fully implemented upon the O 2 DBMS, thus proving the effectiveness of a constraint-based approach for the design of spatial database systems.

One of the most important uses of aggregate queries over data streams is sampling. Typically, aggregation is performed over sliding windows where queries return new results whenever the window contents change, a concept referred to as a continuous query. Existing data models and query languages for streams are not capable of expressing many practical user-defined samplings over streams. To this end we propose a new data stream model, referred to as the sequence model, and a query language for specifying aggregate queries over data streams. We show that the sequence model can readily express a superset of the aggregate queries expressible in the previously proposed time-based data stream model, thus providing a declarative and formal semantics to understand and reason about continuous aggregate queries. Defined on top of the sequence model, our query language supports existing sliding window operators and a novel frequency operator. By using the frequency operator one is capable of expressing useful sampling queries, such as queries with user-defined group-based sampling and nested aggregation over either the input stream or the result stream. Such capabilities are beyond those of previously proposed query languages over streams. Finally, we conduct a preliminary experimental study that shows our language is effective and efficient in practice.

Semistructured data has attracted much attention in database research lately. New data models, query languages and systems have been developed. However, very little research has been done on formally defined semantics for the new data models and query languages. As a consequence, some aspects of the meaning of expressions in the database and answers to queries are not completely understood. In this paper we propose a semantics for semistructured data based on a modal logic framework used to give semantics to feature structures. A semistructured database is seen as inducing a certain model, which is then used to evaluate queries posed to the database in a first-order language. The main advantage of this approach is to give a formal basis for analyzing some possibly conflicting views of semistructured data as a model for partial information.KeywordsModal LogicQuery LanguageComplex ObjectOutgoing EdgeHybrid LogicThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supporting large volumes of multi-dimensional data is an inherent characteristic of modern database applications, such as geographical information systems (GIS), and image and multimedia databases. such databases need underlying systems with extended features like query languages, data models, and indexing methods, as compared to traditional databases, mainly because of the complexity of representing and retrieving data. In particular, spatio-temporal information systems must efficiently store and access potentially very large quantities of spatial and temporal data so we have developed index structures that can capture the time varying nature of moving objects, namely spatio-temporal structures. This paper describes the design of a query system for such data.

The management of the magnanimous spatial data needs the distributed cooperative GIS platform. This paper proposed the Peer To Peer and Cooperating Computing Hybrid Discovering Model of Distributed Geospatial Information. Based on the (Spatial Resource Discovery Agent (SRDA) and ordinary neighbor node, Combined with Spatial data model of MAPGIS7.0, this article brings up the geographic structure query language in P2P network (P2PGSQL), and has designed the interpreter of P2PGSQL. In the Peer To Peer and Cooperating Computing Hybrid Discovering Model, this paper has designed the algorithms that construct the distributed hash table and decrease the delay. By discovering and performing the potential swaps that are beneficial to the match between overlay and physical network, it can reduce the average latency and improve the performance of the system. Based on the P2P Cooperating Computing Hybrid Discovering Model, this article has given the method of the P2PGSQL in grid environment and built the mapping model of the global spatial query sentence to the local query sentence. With constituting a global spatial data resource catalog to record the distributing information of all spatial database on each node, P2PGSQL has backed up effective expression of spatial database management mission in grid environment. Hence, P2PGSQL can sustain the query of distribute spatial data in grid environment. This article has extended the grid spatial relation operation (Intersection, Union, Difference) and synthesized operation (Length, Buffer, intersecting region) and so on. The P2PGSQL can reduce the quantity of spatial data transmitting among nodes and enhance the capacity of parallel operation. At the same time, P2PGSQL can also sustain multi-spatial data model by the extension of structure data on the data type. At the end, an experimental instance of P2PGSQL has been carried out based on mapgis7.0 platform.

Relational data modeling can often be restrictive as it provides no direct facility for modeling polymorphic types, reified relations, multi-valued attributes, and other common high-level structures in data. This creates many challenges in data modeling and engineering tasks, and has led to the rise of more flexible NoSQL databases, such as graph and document databases. In the absence of structured schemas, however, we can neither express nor validate the intention of data models, making long-term maintenance of databases substantially more difficult. To resolve this dilemma, we argue that, parallel to the role of classical predicate logic for relational algebra, contemporary foundations of mathematics rooted in type theory can guide us in the development of powerful new high-level data models and query languages. To this end, we introduce a new polymorphic entity-relation-attribute (PERA) data model, grounded in type-theoretic principles and accessible through classical conceptual modeling, with a near-natural query language: TypeQL. We illustrate the syntax of TypeQL as well as its denotation in the PERA model, formalize our model as an algebraic theory with dependent types, and describe its stratified semantics.

Service composition is a major challenge in an open environment populated by large numbers of heterogeneous services. In such a setting, the efficient interaction of directory-based service discovery with service composition engines is crucial. In this article we present a Java-based directory that offers special functionality enabling effective service composition. In order to optimize the interaction of the directory with different service composition algorithms exploiting application-specific heuristics, the directory supports user-defined selection and ranking functions written in a declarative query language. Inside the directory queries are transformed and compiled to JVM bytecode which is dynamically linked into the directory. The compiled query enables a best-first search of matching directory entries, efficiently pruning the search space.

Aufgrund fallender Preise zur Speicherung von Daten kann man derzeit eine explosionsartige Zunahme in der Menge der verfügbaren Daten beobachten. Diese Entwicklung gibt Unternehmen und wissenschaftliche Institutionen die Möglichkeit empirische Daten in ungekannter Größenordnung zu analysieren. Für viele Firmen ist die Analyse der gesammelten Daten aus ihrem operationalen Geschäft längst zu einem zentralen strategischen Aspekt geworden. Im Gegensatz zu der seit längerem schon betriebenen Business Intelligence, bestehen diese Analysen nicht mehr nur aus traditionellen relationalen Anfragen. In zunehmendem Anteil kommen komplexe Algorithmen aus den Bereichen Data Mining und Maschinelles Lernen hinzu, um versteckte Muster in den Daten zu erkennen, oder Vorhersagemodelle zu trainieren. Mit zunehmender Datenmenge und Komplexität der Analysen wird jedoch eine neue Generation von Systemen benötigt, die diese Kombination aus Anfragekomplexität und Datenvolumen gewachsen sind. Relationale Datenbanken waren lange Zeit das Zugpferd der Datenanalyse im großen Stil. Grund dafür war zum großen Teil ihre deklarativen Anfragesprache, welche es ermöglichte die logischen und physischen Aspekte der Datenspeicherung und Verarbeitung zu trennen, und Anfragen automatisch zu optimieren. Das starres Datenmodell und ihre beschränkte Menge von möglichen Operationen schränken jedoch die Anwendbarkeit von relationalen Datenbanken für viele der neueren analytischen Probleme stark ein. Diese Erkenntnis hat die Entwicklung einer neuen Generation von Systemen und Architekturen eingeläutet, die sich durch sehr generische Abstraktionen für parallelisierbare analytische Programme auszeichnen; MapReduce kann hier beispielhaft genannt werden, als der zweifelsohne prominenteste Vertreter dieser Systeme. Zwar vereinfachte und erschloss diese neue Generation von Systemen die Datenanalyse in diversen neuen Anwendungsfeldern, sie ist jedoch nicht in der Lage komplexe Anwendungen aus den Bereichen Data Mining und Maschinelles Lernen effizient abzubilden, ohne sich dabei extrem auf spezifische Anwendungen zu spezialisieren. Verglichen mit den relationalen Datenbanken haben MapReduce und vergleichbare Systeme außerdem die deklarative Abstraktion aufgegeben und zwingen den Anwender dazu systemnahe Programme zu schreiben und diese manuell zu optimieren. In dieser Dissertation werden verschiedene Techniken vorgestellt, die es ermöglichen etliche der zentralen Eigenschaften von relationalen Datenbanken im Kontext dieser neuen Generation von daten-parallelen Analysesystemen zu realisieren. Mithilfe dieser Techniken ist es möglich ein Analysesystem zu beschreiben, dessen Programme gleichzeitig sowohl generische und ausdrucksstark, als auch prägnant und deklarativ sind. Im einzelnen stellen wir folgende Techniken vor: Erstens, eine Programmierabstraktion die generisch ist und mit komplexen Datenmodellen umgehen kann, aber gleichzeitig viele der deklarativen Eigenschaften der relationalen Algebra erhält. Programme, die gegen dies Abstraktion entwickelt werden können ähnlich optimiert werden wie relationale Anfragen. Zweitens stellen wir eine Abstraktion für iterative daten-parallele Algorithmen vor. Die Abstraktion unterstützt inkrementelle (delta-basierte) Berechnungen und geht mit zustandsbehafteteten Berechnungen transparent um. Wir beschreiben wie man einen relationalen Anfrageoptimierer erweitern kann so dass dieser iterative Anfragen effektiv optimiert. Wir zeigen dabei dass der Optimierer dadurch in die Lage versetzt wird automatisch Ausführungspläne zu erzeugen, die wohlbekannten, manuell erstellten Programmen entsprechen. Die Abstraktion subsumiert dadurch spezialisierte Systeme (wie Pregel) und bietet vergleichbare Performanz. Drittens stellen wir Methoden vor, um die Programmierabstraktion in eine funktionale Sprachen einzubetten. Diese Integration ermögliche es prägnante Programme zu schreiben und einfach wiederzuverwendenden Komponenten und Bibliotheken, sowie Domänenspezifische Sprachen, zu erstellen. Wir legen dar wie man die Übersetzung und Optimierung des daten-parallelen Programms mit dem Sprachübersetzer der funktionalen Sprache so integriert, dass maximales Optimierungspotenzial besteht.

This paper describes the environment, the issues and the progress made in developing a database management system for managing data generated by fracture study experiments in polymers and materials engineering. The experiment data produced by materials engineers is large and has several data types that include summary data, image data, spatial data, and temporal data obtained by applying differing amounts of multiaxial stresses to materials at different times. The types of queries on the experiment data include statistical queries, matching queries, mapping queries, and spatiotemporal queries. We discuss the data model, query language, and spatial data structure issues associated with our environment, and how they translate into specific research problems. We also briefly summarize the progress made in each area.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Alternatives for facilitating automatic transformation of BIM data using semantic query languages