MoodView

Article Free Access Share on Processing queries for first-few answers Authors: Roberto J. Bayardo Department of Computer Sciences and Applied Research Laboratories, The University of Texas at Austin Department of Computer Sciences and Applied Research Laboratories, The University of Texas at AustinView Profile , Daniel P. Miranker Department of Computer Sciences and Applied Research Laboratories, The University of Texas at Austin Department of Computer Sciences and Applied Research Laboratories, The University of Texas at AustinView Profile Authors Info & Claims CIKM '96: Proceedings of the fifth international conference on Information and knowledge managementNovember 1996 Pages 45–52https://doi.org/10.1145/238355.238372Online:12 November 1996Publication History 24citation256DownloadsMetricsTotal Citations24Total Downloads256Last 12 Months4Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Integration of object-oriented programming languages and database systems in KOPERNIK

This paper describes the design and the implementation of OOPE, the graphical programming environment of the prototype version of O2, an object-oriented database system. One of the distinguishing features of this environment is that it mixes the functionalities of programming environments, of both databases and programming languages. Thus, it facilitates and fastens not only the schema design, but also the development of application programs. Another interesting characteristic is that it is being developed using as much as possible the functionalities provided by the O2 system, namely the programming language, the database and the graphics functionalities.

OdeView is the graphical front end for Ode, an object-oriented database system and environment. Ode's data model supports data encapsulation, type inheritance, and complex objects. OdeView provides facilities for examining the database schema (i.e., the object type or class hierarchy), examining class definitions, browsing objects, following chains of references starting from an object, synchronized browsing, displaying selected portions of objects (projection), and retrieving objects with specific characteristics (selection). OdeView does not need to know about the internals of Ode objects. Consequently, the internals of specific classes are not hardwired into OdeView and new classes can be added to the Ode database without requiring any changes to or recompilation of OdeView. Just as OdeView does not know about the object internals, class functions (methods) for displaying objects are written without knowing about the specifics of the windowing software used by OdeView or the graphical user interface provided by it. In this paper, we present OdeView, and discuss its design and implementation.

Object-oriented database systems (OODBs) need efficient support for manipulation of complex objects. In particular, support of queries involving evaluations of set predicates is often required in handling complex objects. In this paper, we propose a scheme to apply signature file techniques, which were originally invented for text retrieval, to the support of set value accesses, and quantitatively evaluate their potential capabilities. Two signature file organizations, the sequential signature file and the bit-sliced signature file, are considered and their performance is compared with that of the nested index for queries involving the set inclusion operator (⊆). We develop a detailed cost model and present analytical results clarifying their retrieval, storage, and update costs. Our analysis shows that the bit-sliced signature file is a very promising set access facility in OODBs.

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.

Hybrid transactional and analytical processing (HTAP) systems like SAP HANA make it much simpler to manage both operational load and analytical queries without ETL, separate data warehouses, et al. To represent both transactional and analytical business logic in a single database system, stored procedures are often used to express analytical queries using control flow logic and DMLs. Optimizing these complex procedures requires a fair knowledge of imperative programming languages as well as the declarative query language. Therefore, unified optimization techniques considering both program and query optimization techniques are essential for achieving optimal query performance. In this paper, we propose a novel unified optimization technique for efficient iterative query processing. We present a notion of query motion that allows the movement of SQL queries in and out of a loop. Additionally, we exploit a new cost model that measures the quality of the execution plan with consideration for queries and loop iterations. We describe our experimental evaluation that demonstrates the benefit of our technique using both a standard decision support benchmark and real-world workloads. An extensive evaluation shows that our unified optimization technique enumerates plans that achieve performance improvements of up to an order of magnitude faster than plans generated by the existing loop-invariant code motion technique.

Distributed computations on graphs are becoming increasingly important with the emergence of large graphs such as social networks and the Web that contain huge amounts of useful information. Computations can be easily distributed with the use of specialised frameworks like Hadoop with MapReduce, Giraph/Pregel or GraphLab. Yet, declarative, query-like, but at the same time efficient solutions are lacking. Programmers are needed to code all computations by hand and manually optimise each individual program.

Cascadia is a system that provides RFID-based pervasive computing applications with an infrastructure for specifying, extracting and managing meaningful high-level events from raw RFID data. Cascadia provides three important services. First, it allows application developers and even users to specify events using either a declarative query language or an intuitive visual language based on direct manipulation. Second, it provides an API that facilitates the development of applications which rely on RFID-based events. Third, it automatically detects the specified events, forwards them to registered applications and stores them for later use (e.g., for historical queries).We present the design and implementation of Cascadia along with an evaluation that includes both a user study and measurements on traces collected in a building-wide RFID deployment. To demonstrate how Cascadia facilitates application development, we built a simple digital diary application in the form of a calendar that populates itself with RFID-based events. Cascadia copes with ambiguous RFID data and limitations in an RFID deployment by transforming RFID readings into probabilistic events. We show that this approach outperforms deterministic event detection techniques while avoiding the need to specify and train sophisticated models.

The system StreamAPAS and its declarative query language allows users to define temporal data analysis. This paper addresses the problem of lack of the continuous language standard. The proposed language syntax indicates how hierarchical data structures simplify working with spatial data and groups of tuple attributes. The query language is also based on object-oriented programming concepts as a result of which continuous processing applications are easier to develop and maintain. In addition, we discuss the problem of a query logic representation. In contrast to relations stored in DBMS, data streams are temporal so that DSMS should be aware of their dynamic characteristics.Streams characteristics can be described using variables such as tuple rates and invariables like monotonicity. In StreamAPAS, a query is represented as a directed acyclic graph (DAG), whose operators define tuple data transmission model and have information of result stream monotonicity associated with them. Even though this representation is still static,this approach enables us to detect optimization points which are crucial from a stream processing viewpoint.

With the existence of large volumes of spatial-keyword data, several query types have emerged to empower many spatial-keyword applications. In this section, we present an overview of the important types of spatial-keyword queries. Typically, in data management systems, queries are expressed using declarative query languages, e.g., SQL [20]. A query is composed of multiple query predicates that are combined using conjunctives, disjunctives, for-all (∀), and there-exists (∃) constructs. For example, in relational databases, these query predicates are the select, join, project, and group-by predicates. Complex queries can be composed as combinations of these query predicates.

The relational calculus (RC) is a concise, declarative query language. However, existing RC query evaluation approaches are inefficient and often deviate from established algorithms based on finite tables used in database management systems. We devise a new translation of an arbitrary RC query into two safe-range queries, for which the finiteness of the query's evaluation result is guaranteed. Assuming an infinite domain, the two queries have the following meaning: The first is closed and characterizes the original query's relative safety, i.e., whether given a fixed database, the original query evaluates to a finite relation. The second safe-range query is equivalent to the original query, if the latter is relatively safe. We compose our translation with other, more standard ones to ultimately obtain two SQL queries. This allows us to use standard database management systems to evaluate arbitrary RC queries. We show that our translation improves the time complexity over existing approaches, which we also empirically confirm in both realistic and synthetic experiments.

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.

S.167-171

Content Based Image Retrieval (CBIR) has been a challenging research area among researchers of both industries and academic institutions. Human can judge and respond by memory to obtain and locate the target image and relate it by specific object, but for computer vision it's a challenging issue to retrieve image from complex database that is difficult to balance with an efficient algorithm. Simultaneous situation and complex object has been always a big challenge to retrieve an object from image or query image from database. In this paper, we propose to combine Neural Network to sense a specific objective image that is content based and easily retrieving from complex database.