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To enable dynamic, extensible, and distributed query processing in super-peer based P2P networks, where standard query operators and user-defined code can be executed nearby the data, we distribute query processing to (super-) peers. Therefore, super-peers provide functionality for the management of the indices, query optimization, and query processing. Additionally, we expect that peers provide query processing capabilities to be full members of the P2P network. To enable this, super-peers have to provide an optimizer for generating efficient query plans from the queries they receive. The distribution process is guided by the routing index which is dynamic and corresponds to the data allocation schema in traditional distributed DBMSs.
In this paper we propose a new framework for dynamic distributed query processing based on so-called HyperQueries which are essentially query evaluation sub-plans “sitting behind” hyperlinks. We illustrate the flexibility of this distributed query processing architecture in the context of B2B electronic market places. Architecting an electronic market place as a data warehouse by integrating all the data from all participating enterprises in one centralized repository incurs severe problems. Using HyperQueries, application integration is achieved via dynamic distributed query evaluation plans. The electronic market place serves as an intermediary between clients and providers executing their sub-queries referenced via hyperlinks. The hyperlinks are embedded within data objects of the intermediary’s database. Retrieving such a virtual object will automatically initiate the execution of the referenced HyperQuery in order to materialize the entire object. Thus, sensitive data remains under the full control of the data providers.
Electronic market places and virtual enterprises have become important applications for query processing [2]. Building a scalable virtual B2B market place with hundreds or thousands participants requires highly flexible, distributed query processing capabilities. Architecting an electronic market place as a data warehouse by integrating all the data from all participating enterprises in one centralized data repository incurs severe problems: - Security and privacy violations: The participants of the market place have to relinquish the control over their data and entrust sensitive information to the market place host. - Coherence problems: The coherence of highly dynamic data, such as availability and shipping information, may be violated due to outdated materialized data in the market place’s data warehouse. - Schema integration problems: Using the warehouse approach all relevant data from all participants have to be converted à priori into the same format. Often, it would be easier to leave the data inside the participant’s information systems, e.g., legacy systems, within the local sites, and apply particular local wrapper operations. This way, data is only converted on demand and the most recent coherent state of the data is returned. - Fixed query operators: In a data warehouse-like electronic market place, all information is converted into materialized data. This is often not desirable in such complex applications like electronic procurement/bidding.
Databases have employed a schema-based approach to store and retrieve structured data for decades. For peer-to-peer (P2P) networks, similar approaches are just beginning to emerge, also motivated by the fact, that sending (atomic) queries to the appropriate peers clearly fails for queries which need data from more than one peer to be executed. While quite a few database techniques can be re-used in this new context, a P2P data management infrastructure poses additional challenges which have to be solved before schema-based P2P networks become as common as schema-based databases. Because of the dynamic nature of P2P networks, we can neither assume global knowledge about data distribution, nor are static topologies and static query plans suitable for these networks. Unlike in traditional distributed database systems, we cannot assume a complete schema instance but rather work with a distributed schema which directs query processing tasks from one node to one or more neighboring nodes.
In this paper, we will first discuss a suitable topology for schema-based P2P networks and how distributed knowledge about data distribution can be stored, accessed and updated based on that topology. Second we will describe how this knowledge can be used to distribute abstract query plans through the P2P network and expand them on the fly such that we can place query operators next to data sources and utilize distributed computing resources more effectively.
Peer-to-Peer infrastructures are emerging as one of the important data management infrastructures in the World Wide Web. So far, however, most work has focused on simple P2P networks which tackle efficient query distribution to a large set of peers but assume that each query can be answered completely at each peer. For queries which need data from more than one peer to be executed this is clearly insufficient. Unfortunately, though quite a few database techniques can be re-used in the P2P context, P2P data management infrastructures pose additional challenges caused by the dynamic nature of these networks. In P2P networks, we can assume neither global knowledge about data distribution, nor the suitableness of static topologies and static query plans for these networks. Unlike in traditional distributed database systems, we cannot assume complete information schema and allocation schema instances but rather work with distributed schema information which can only direct query processing tasks from one node to one or more neighboring nodes.
In this paper we first describe briefly our super-peer based topology and schema-aware distributed routing indices extended with suitable statistics and describe how this information is extracted and updated. Second we show how these indices facilitate the distribution and dynamic expansion of query plans. Third we propose a set of transformation rules to optimize query plans and discuss different optimization strategies in detail, enabling efficient distributed query processing in a schema-based P2P network.
Zentrum für Exzellenz
(2024)
In this work we present novel query evaluation techniques for data integration systems in different environments, ranging from a central data-warehouse approach, over distributed virtual market places, to peer-to-peer (P2P) systems. Based on a new distributed evaluation technique, the so-called HyperQueries, we present a reference architecture for distributed virtual market places. These HyperQueries enable us to dynamically construct query evaluation plans by referencing sub-plans in the Internet. Furthermore, the process of data integration is structured. Subsequently, we investigate P2P data integration systems without central instances. We introduce so-called Super-Peers which structure a P2P network. Using this Super-Peer based network we "unroll" queries. This allows us to execute even user-defined operators nearby the data sources. Finally, we propose novel, efficient join algorithms for decision support queries in central data-warehouse systems. The proposed order-preserving hashjoins and generalized hashteams are based on early sorting and early partitioning of the inputs and can speed up the query evaluation up to orders of magnitutes.
Schwenkbiegen ist ein etabliertes Umformverfahren, bei dem Materialverlust vermieden und Ressourcen effizient genutzt werden. Der Prozess erfordert jedoch aufwändige Optimierungen, die bisher stark vom Fachwissen der Bediener abhängen. Dies führt zu hohem Zeit- und Materialaufwand, da Optimierungsschritte iterativ erfolgen. Angesichts des Fachkräftemangels ist eine technologische Aufrüstung der Anlagen im Sinne von Industrie 4.0 notwendig. Im Rahmen eines Projekts wurden mittels intelligenter Sensorik kritische Einflussfaktoren erfasst, die Korrelationen zwischen Produktfehlern und Anlagenverformungen aufzeigen. Darauf basierend wurde eine Methodik entwickelt, die die Grundlage für eine Inline-Kompensation schafft, bei der die Anlage eigenständig Prozessparameter anpasst, um Produktfehler zu korrigieren und perspektivisch eine fehlerfreie Fertigung ab dem ersten Bauteil zu ermöglichen.
Digitalisierungsprojekte helfen dem Anwender, komplexe Prozesse einfacher und effizienter darzustellen. Allerdings gibt es viele Hemmnisse, welche die Umsetzung deutlich erschweren. Zurückhaltung bei der Umsetzung ist spürbar. Dies trifft unter anderem Arbeitgeber und Arbeitnehmer, die durch das Warten oder Vermeiden ins ökonomische Abseits geraten können. Diese Beobachtungen lassen sich auf eine übergeordnete wissenschaftliche Leitfrage zurückführen: Welche Barrieren und systemischen Herausforderungen erschweren eine nachhaltige Transformation im Rahmen von Industrie 4.0, insbesondere unter Berücksichtigung menschlicher Arbeit in der Produktionstechnik? Welche Fragen stellen sich die betroffenen Akteure? Das wesentliche Ziel dieser langfristig ausgelegten Forschungsarbeit ist es, diese Fragen dezidiert und im Detail zu konkretisieren, um daraus ein konzeptionelles Fundament zu entwickeln, das Forschung, Lehre und technologische Entwicklung integriert und die Potenziale digitaler Technologien mit dem Erfahrungs- und Handlungswissen der Beschäftigten in der Produktion langfristig synergetisch verbindet.
Das Gewindeformen erfordert eine präzise Schmierstoffapplikation, da hohe Flächenpressungen und lokale Temperaturspitzen die Werkzeugbelastung erheblich beeinflussen. Aktuelle Sprüh- und Minimalmengenschmierungssysteme (MMS) weisen trotz etablierter Technik häufig Streuverluste, unzureichende Benetzung und instabile Tropfendynamik auf. Diese wissenschaftliche Betrachtung beinhaltet und untersucht einen integrativen Ansatz zur adaptiven Präzisionsbeölung beim Gewindeformen, der auf Computational Fluid Dynamics (CFD)-basierter Strömungsanalyse, experimenteller Validierung und Künstliche Intelligenz (KI)-gestützten Optimierungsverfahren basiert. Im Fokus stehen Tropfengröße, Strahlgeometrie, Düsenposition und Umgebungsströmung sowie deren Einfluss auf die Benetzungsintensität. Erste simulationsgestützteVoruntersuchungen zeigen das Potenzial einer datenbasierten Optimierung zur Reduktion von Benetzungsdefiziten und zur Auslegung künftiger Regelstrategien für eine ressourceneffiziente Schmierstoffapplikation.