Contact

Marcus Pinnecke, M.Sc.

Faculty of Computer Science
Datenbanken & Software Engineering
Universitätsplatz 2, G29-125
39106, Magdeburg, Germany

Phone 0391 67-52845
Fax 0391 67-12020
Mail marcus.pinnecke@ovgu.de

 
 
 
 
 
 
 
 
 
 
 
 
  One Level Up

Active
Projects

Protobase

The Prototyping Platform for Analytic Microservice

Code Repository Slack Channel Related Publications Docs More


Hermes

Explore scholar citation networks

Code Repository Slack Channel Related Publication Docs More


ycsbX

Benchmark HTAP database systems, efficiently

Code Repository Slack Channel Related Publications Docs More




Retired
Projects



FeatureIDE

An Extensible Framework for Feature-Oriented Software Development

Code Repository Slack Channel Related Publication Docs More


Java Event Processing Connectivity

A Middleware to Unify, Simplify and Enrich Event Processing in Java

Code Repository Slack Channel Related Publication Docs More


XXL - eXtensible and fleXible Library

A Java Library containing a Rich Infrastructure for Advanced Query Processing Functionality

Code Repository Slack Channel Publications Docs More


Experimental Games Generator

Web Software Platform for Developing and Conducting Scientific Experiments in Social Sciences

Code Repository Slack Channel Related Publication Docs More


Tweak GUI

Software Utility for Microsoft Windows to Manage, Configure, and Optimize System Settings

Code Repository Slack Channel Mentionings Docs More


Acquired

Scalable Hardware-Aided Trusted Data Management (STAN)

Scientific project "Schwerpunktprogramm (SPP)", funded by Deutsche Forschungsgemeinschaft (DFG)

Details


Adaptive Data Management in Evolving Heterogeneous Hardware/Software Systems (ADAMANT)

Scientific project "Schwerpunktprogramm (SPP)", funded by Deutsche Forschungsgemeinschaft (DFG)

Details


MetaProteomeAnalyzer Service (MetaProtServ)

Scientific project, funded by Deutsche Forschungsgemeinschaft (DFG)

Details


COOPeR: Cross-device OLTP/OLAP PRocessing

Scientific project, funded by Deutsche Forschungsgemeinschaft (DFG)

Details


Hugo Junkers Preis 2018

For FeatureIDE, category most innovative alliance

Details


Upcoming
Publications

Efficient String Encoding in the Many-Core Age
Upcoming

Marcus Pinnecke, Gabriel Campero Durand, Johann Wagner, Johannes Wuensche, Marten Wallewein-Eising and Gunter Saake.
PDF Cite Slides


ycsbX: Online-Benchmarking of Hybrid Transactional/Analytical
Data Management Systems
Upcoming

Marcus Pinnecke, Gabriel Campero Durand, Iya Arefyeva, Mahmoud Mohsen, Roman Zoun, David Broneske and Gunter Saake.
PDF Cite Slides


GridTables: A One-Size-Fits-Most Data Store Architecture for
GPU-Powered HTAP Databases
Upcoming

Marcus Pinnecke, David Broneske, Gabriel Campero Durand, Roman Zoun, and Gunter Saake.
PDF Cite Slides


Main-Authored
Publications

Backend/Database Co-Design for Rapid Webservice Prototyping:
The FIN Doktorandentag Talk

Marcus Pinnecke
Self-published extended abstract


Read Slides


Protobase: It's About Time for Backend/Database Co-Design
A Demo on Rapid Microservice Prototyping for Third-Party Dataset Analytics

Marcus Pinnecke, Gabriel Campero Durand, Roman Zoun, David Broneske, and Gunter Saake.
In Datenbanksysteme für Business, Technologie und Web (BTW 2019), Demo, Accepted


In this interactive demonstration, we show the current state of Protobase, our main-memory analytic document store that is designed from scratch to enable rapid prototyping of efficient microservices that perform analytics and explorations on (third-party) JSON-like documents stored in a novel columnar binary-encoded format, called the Cabin file format. In contrast to other solutions, our database system does neither expose a particular query language, nor a fixed REST API to its clients. Instead, the entire user-defined backend logic, which user code is written in Python, is placed inside a sandbox that runs in the systems process. Protobase in turn exposes a user-defined REST API that the (frontend) application interacts with. Thus, our system acts as a backend server while at the same time avoids full exposure of its database to the clients. Consequentially, a Protobase instance (database + user code + REST API) serves as (the entire) microservice - potentially minimizing the number of systems running in a typical analytic software stack. In terms of execution performance, Protobase therefore takes the inter-process communication overhead between backend and database system out of the picture and heavily utilizes columnar binary document storage to scale-up for analytic queries. Both features lead to a notable performance gain for non-trivial services, potentially minimizing the number of required nodes in a cloud setting, too. In our demo, we overview Protobases internals, spot major design decisions, and show how to prototype a scholarly search engine managing the Microsoft Academic Graph, a real-world scientific paper graph of roughly 154 mio. documents.
Read in BTW 2018 Cite Slides


Are Databases Fit for Hybrid Workloads on GPUs?
A Storage Engine’s Perspective.

Marcus Pinnecke, David Broneske, Gabriel Campero Durand, and Gunter Saake.
In Proceedings of the International Workshop on Big Data Management on Emerging Hardware,
San Diego, USA, April 22, 2017, pages 1599–1606, 2017

Employing special-purpose processors (e.g., GPUs) in database systems has been studied throughout the last decade. Research on heterogeneous database systems that use both general- and special-purpose processors has addressed either transaction- or analytic processing, but not the combination of them. Support for hybrid transaction- and analytic processing (HTAP) has been studied exclusively for CPU-only systems. In this paper we ask the question whether current systems are ready for HTAP workload management with cooperating general-and special-purpose processors. For this, we take the perspective of the backbone of database systems: the storage engine. We propose a unified terminology and a comprehensive taxonomy to compare state-of-the-art engines from both domains. We show similarities and differences, and determine a necessary set of features for engines supporting HTAP workload on CPUs and GPUs. Answering our research question, our findings yield a resolute: not yet.
Read in ICDE 2017 Cite Slides


Efficient Single Step Traversals in Main-Memory Graph-Shaped Data

Marcus Pinnecke
Master's thesis, University of Magdeburg,
June 2016

Management of graph-shaped data gained a momentum to both industry and research. Traversal queries through a graph-shaped dataset are easy to express, and can be efficiently executed using graph databases. High-performance traversals through graph-shaped data is claimed to be enabled by native graph storage (i.e., encoding data using graph data structures), and native graph processing (i.e., operating on data with graph-domain specific operations). A common belief is that native graph storage databases are inherently superior to non-native graph storage databases (e.g., relational databases) in terms of traversal efficiency. This claim is especially supported by graph database vendors, but not yet proven or disproven objectively. In this work, we study in context of main-memory systems how the primitives of arbitrary traversal algorithms (i.e., single step traversal queries) are affected by native graph storage, and non-native graph storage in terms of execution performance. We focus on single step traversal queries that address navigation in graph-shaped data. We compare classic graph encoding and a state-of-the-art graph database micro-index as representatives of native graph storage, and table scanning and indexing by several binary search trees as representatives of non-native graph storage. We evaluate the representatives for native and non-native graph storage on both artificial datasets, and real world graph datasets. To be aware of confounding variables, we implement a unified main-memory-only experimental query engine to avoid bias from internal behavior of some blackbox systems (e.g., main-memory systems vs. disk-based systems). Our experimental results show that high efficient traversal algorithm in main-memory systems require indexing adjacent records and incident relationships rather than the property of being a native graph storage or a non-native graph storage.

Read Cite Slides


Toward GPU Accelerated Data Stream Processing

Marcus Pinnecke, David Broneske, and Gunter Saake
In Proceedings of the 27th GI-Workshop Grundlagen von Datenbanken, Gommern, Germany,
May 26-29, 2015., pages 78–83, 2015

In recent years, the need for continuous processing and analysis of data streams has increased rapidly. To achieve high throughput-rates, stream-applications make use of operator-parallelization, batching-strategies and distribution. Another possibility is to utilize co-processors capabilities per operator. Further, the database community noticed, that a column-oriented architecture is essential for efficient co-processing, since the data transfer overhead is smaller compared to trans- ferring whole tables. However, current systems still rely on a row-wise architec- ture for stream processing, because it requires data structures for high velocity. In contrast, stream portions are in rest while being bound to a window. With this, we are able to alter the per-window event representation from row to column orientation, which will enable us to exploit GPU acceleration. To provide general-purpose GPU capabilities for stream processing, the varying window sizes lead to challenges. Since very large windows cannot be passed directly to the GPU, we propose to split the variable-length windows into fixed-sized window portions. Further, each such portion has a column-oriented event representation. In this paper, we present a time and space efficient, data corruption free concept for this task. Finally, we identify open research challenges related to co-processing in the context of stream processing.

Read in GvDB Cite Slides


Query Optimization in Heterogenous Event Processing Federations

Marcus Pinnecke and Bastian Hoßbach
Datenbank-Spektrum, 15(3):193–202, 2015

Continuous processing of event streams evolved to an important class of data management over the last years and will become even more important due to novel applications such as the Internet of Things. Because systems for data stream and event processing have been developed independent of each other, often in competition and without the existence of any standards, the Stream Processing System (SPS) landscape is extremely heterogeneous today. To overcome the problems caused by this heterogeneity, a novel event processing middleware, the Java Event Processing Connectivity (JEPC), has been presented recently. However, despite the fact that SPSs can be accessed uniformly using JEPC, their different performance profiles caused by different algorithms and implementations remain. This gives the opportunity to query optimization, because individual system strengths can be exploited. In this paper, we present a novel query optimizer that exploits the technical heterogeneity in a federation of different unified SPSs. Taking into account different performance profiles of SPSs, we address query plan partitioning, candidate selection, and reducing inter-system communication in order to improve the overall query performance. We suggest a heuristic that finds a good initial mapping of sub-plans to a set of heterogenous SPSs. An experimental evaluation clearly shows that heterogeneous federations outperform homogeneous federations, in general, and that our heuristic performs well in practice.

Open in Datenbankspektrum Cite


Konzept und prototypische Implementierung eines föderativen Complex Event Processing Systeme mit Operatorverteilung

Marcus Pinnecke
In Datenbanksysteme für Business, Technologie und Web (BTW 2015),
Workshopband, pages 233–242. GI, 2015

Complex Event Processing (CEP) ist eine etablierte Technologie zur Verarbeitung von Ereignisstr¨omen in nahezu Echtzeit. Trotz alledem unterscheiden bestehende CEP-Systeme sich stark in ihren jeweiligen Leistungsumfaengen und -profilen. Werden verschiedene CEP-Systeme in einem foederativen System kombiniert, so kann das resultierende System im Vergleich zu den Einzelsystemen eine hoehere Datendurchsatzrate und einen breiteren Leistungsumfang erreichen. Fehlende Standardisierung und inkompatible Schnittstellen der CEP-Systeme behindern diesen Ansatz jedoch. Dieser Missstand wird durch die Middleware Java Event Processing Connectivity (JEPC) behoben. Gegenstand dieser Arbeit ist das auf JEPC basierendes foederatives CEP-System so zu erweitern, dass eine Verteilung von Anfrageoperatoren auf die beteiligten Systeme automatisch durchfuehrt wird. Hierfuer wird in dieser Arbeit das zugrundeliegende Konzept, eine Anfrageoptimierung sowie ein Kostenmodell fuer die Auswahl eines konkreten Systems vorgestellt. In einer Evaluation wird gezeigt, dass durch den Einsatz einer Foederation die Datendurchsatzrate deutlich steigt, insbesondere wenn es sich um eine heterogene Foederation mit unterschiedlichen Leistungsprofilen handelt.

Read in BTW Cite Slides

Supervisioned
Publications

Memory Management Strategies in CPU/GPU Database Systems:
A Survey

Iya Arefyeva, David Broneske, Gabriel Campero, Marcus Pinnecke, and Gunter Saake.
In BDAS. Springer,
September 2018

GPU-accelerated in-memory database systems have gained a lot of popularity over the last several years. However, GPUs have limited memory capacity, and the data to process might not fit into the GPU memory entirely and cause a memory overflow. Fortunately, this problem has many possible solutions, like splitting the data and processing each portion separately, or storing the data in the main memory and transferring it to the GPU on demand. This paper provides a survey of four main techniques for managing GPU memory and their applications for query processing in cross-device powered database systems.

Read in BDAS Cite Slides


Low-Latency Transaction Execution on Graphics Processors:
Dream or Reality?

Iya Arefyeva, Gabriel Campero Durand, Marcus Pinnecke, David Broneske, and Gunter Saake
Ninth International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures (ADMS)
August 2018

In this paper we take a close look into the role of GPUs for executing OLTP workloads, with a focus on CRUD operator-based processing, as opposed to more complex OLTP transactions. To this end we develop a prototype system supporting GPU and CPU variants of DSM and NSM processing, with a delegation-based approach that uses a single-thread scheduler to manage concurrency control, enabling reads with guaranteed bounded staleness. We evaluate our prototype using workloads from the Yahoo! cloud serving benchmark. We report the impact of layout choices, batching configuration and concurrency control designs. Through our study we are able to pinpoint that the contradicting needs in GPU processing for small batches to reduce waiting time, but large batches to reduce execution time, is the essential challenge for OLTP on these processors, affecting all design choices we study. Hence, we propose two preconditions for supporting OLTP with GPUs, aiming to guide researchers in finding scenarios for extending the applicability of GPUs in supporting data management tasks.

Read in ADMS Cite Slides


Column vs. Row Stores for Data Manipulation in Hardware Oblivious CPU/GPU Database Systems

Iya Arefyeva, David Broneske, Marcus Pinnecke, Mudit Bhatnagar, and Gunter Saake.
In GvDB, pages 24–29.
CEUR-WS, 2017

Finding the right storage model (i.e., row-wise or column-wise storage) is an important task for a database system, because each storage model has its best supported application. Moreover, if we consider the usage of a co-processor (e.g., a GPU), another dimension opens up that influences the selection of the storage model. In fact, factors such as favored memory access pattern of the device and data transfer costs play a vital role in a hybrid CPU/GPU system, influencing the optimal storage model. Since there is currently no evaluation of when to use a column or row store for data manipulation (i.e., we look at insert/update/project operators) in a hybrid CPU/GPU system, we present a framework in OpenCL that we use to investigate the break-even points that determine when to use which storage model.

Read in GvDB Cite Slides


Co-Authored
Publications

SIMD Vectorized Hashing for Grouped Aggregation

Bala Gurumurthy, David Broneske, Marcus Pinnecke, Gabriel Campero Durand, and Gunter Saake.
In Advances in Databases and Information Systems
pages 113 – 126, September 2018.

Grouped aggregation is a commonly used analytical function. The common implementation of the function using hashing techniques suffers lower throughput rate due to the collision of the insert keys in the hashing techniques. During collision, the underlying technique searches for an alternative location to insert keys. Searching an alternative location increases the processing time for an individual key thereby degrading the overall throughput. In this work, we use Single Instruction Multiple Data (SIMD) vectorization to search multiple slots at an instant followed by direct aggregation of results. We provide our experimental results of our vectorized grouped aggregation with various open-addressing hashing techniques using several dataset distributions and our inferences on them. Among our findings, we observe different impacts of vectorization on these techniques. Namely, linear probing and two-choice hashing improve their performance with vectorization, whereas cuckoo and hopscotch hashing show a negative impact. Overall, we provide in this work a basic structure of a dedicated SIMD accelerated grouped aggregation framework that can be adapted with different hashing techniques.

Read in ADBIS Cite Slides


GridFormation: Towards Self-Driven Online Data Partitioning using Reinforcement Learning

Gabriel Campero Durand, Marcus Pinnecke, Rufat Piriyev, Mahmoud Mohsen, David Broneske, Gunter Saake, Maya Sekeran, Fabian Rodriguez, and Laxmi Balami.
In First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM)
June 2018

In this paper we define a research agenda to develop a general framework supporting online autonomous tuning of data partitioning and layouts with a reinforcement learning formulation. We establish the core elements of our approach: agent, environment, action space and supporting components. Externally predicted workloads and the current physical design serve as input to our agent. The environment guides the search process by generating immediate rewards based on fresh cost estimates, for either the entirety or a sample of queries from the workload, and by deciding the possible actions given a state. This set of actions is configurable, enabling the representation of different partitioning problems. For use in an online setting the agent learns a fixed-length sequence of n actions that maximize the temporal reward for the predicted workload. Through an initial implementation we assert the feasibility of our approach. To conclude, we list open challenges for this work.

Read in aiDM Cite Slides


Piecing Together Large Puzzles, Efficiently:
Towards Scalable Loading into Graph Database Systems

Gabriel Campero Durand, Jingyi Ma, Marcus Pinnecke, and Gunter Saake.
In Grundlagen von Datenbanken, GvDB
May 2018

Many applications rely on network analysis to extract business intelligence from large datasets, requiring specialized graph tools such as processing frameworks (e.g. Apache Giraph, Gradoop), database systems (e.g. Neo4j, JanusGraph) or applications/libraries (e.g. NetworkX, nvGraph). A recent survey reports scalability, particularly for loading, as the foremost practical challenge faced by users. In this paper we consider the design space of tools for efficient and scalable graph bulk loading. For this we implement a prototypical loader for a property graph DBMS, using a distributed mes- sage bus. With our implementation we evaluate the impact and limits of basic optimizations. Our results confirm the expectation that bulk loading can be best supported as a server-side process. We also find, for our specific case, gains from batching writes (up to 64x speedups in our evaluation), uniform behavior across partitioning strategies, and the need for careful tuning to find the optimal configuration of batching and partitioning. In future work we aim to study loading into alternative physical storages with GeckoDB, an HTAP database system developed in our group.

Read in GvDB Cite Slides


Exploring Large Scholarly Networks with Hermes

Gabriel Campero Durand, Anusha Janardhana, Marcus Pinnecke, Yusra Shakeel, Jacob Krüger, Thomas Leich, and Gunter Saake
In International Conference on Extending Database Technology, EDBT
pages 650–653. OpenProceedings, March 2018

Every year, the number of scientific publications increases, adding complexity to the networks of collaborations, citations, and topics, in which papers are embedded. Analyzing these networks with efficient tools is important to help researchers identify relevant works and understand scientific impact. However, available tools face several limitations, indicating that there is still room for improvement. We present Hermes, a prototype for exploring large and heterogeneous scholarly networks. Hermes allows users to seamlessly navigate diverse types of networks within a single graph, spanning hundreds of millions of nodes and relationships. Our prototype achieves reasonable responsiveness on commodity hardware through: a) comprehensive indexing, b) a careful coupling of a graph database and a search engine, and c) incremental processing of temporal queries. In this demonstration, we explain the techniques we adopt and illustrate how to use Hermes for exploring the Microsoft Academic Graph.

Read in EDBT Cite Poster


Backlogs and Interval Timestamps: Building Blocks for Supporting Temporal Queries in Graph Databases

Gabriel Campero Durand, Marcus Pinnecke, David Broneske, and Gunter Saake.
In Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017)
Venice, Italy, March 21-24, 2017., volume 1810. CEUR-WS, 2017

The analysis of networks, either at a single point in time or through their evolution, is an increasingly important task in modern data management. Graph databases are uniquely suited to improve static network analysis. However, there’s still no consensus on how to best model data evolution with these databases. In our work we propose an elementary concept to support temporal analysis with property graph databases, using a single-graph model limited to structural changes. We manage the temporal aspects of items with interval timestamps and backlogs. To include backlogs in the model we examine two alternatives: (1) global indexes, and (2) using the graph as an index by resorting to timestamp denormalization. We evaluate density calculation and time slice retrieval over successive days from a SNAP dataset, on an Apache Titan prototype of our model, observing from 2x to 100x response time gains by comparing differential vs. snapshot methods; and no conclusive difference between the backlog alternatives.

Read in EDBT Cite Slides


Technical
Reports

Thread Management for High Performance Database Systems:
Design and Implementation

Robert Jendersie, Johannes Wuensche, Johann Wagner, Marten Wallewein-Eising,
Marcus Pinnecke and Gunter Saake
Arbeitsgruppe Database and Software Engineering, Otto-von-Guericke-University Magdeburg

Processing tasks in parallel is used in nearly all applications to keep up with the requirements of modern software systems. However, the current implementation of parallel processing in GeckoDB, a graph database system developed in our group, requires to spawn many short-lived threads that execute a single task and then terminate. This creates a lot of overhead since the threads are not being reused. To counter this effect, we implemented a thread pool to be used in GeckoDB to reduce the additional time required to create and close thousands of threads. In this paper, we show our implementation of a thread pool to process independent tasks in parallel, including the waiting for a set of processed tasks. We also compare the thread pool implementation against the current one. Additionally, we show that the task and thread pool configuration, depending on the use case, has a high impact on the thread pool performance. At the end of our evaluation, we show that the implementation fulfils all given requirements and generally reduce overhead compared to creating and spawning threads individually.

Read in FIN Cite Slides


Theses Supervision

2019

Immanuel Andreas. (Untitled). Master Thesis,
Otto-von-Guericke University Magdeburg, Germany, 2019. In progress.

2018

Rajan Bhardwaj. Model-Driven Layout Adaption for HTAP Workloads. Master Thesis,
Otto-von-Guericke University Magdeburg, Germany, August 2018.
Anusha Sirigeri. Advanced Optimization Techniques for Relational Database Operator on Modern Hardware. Master Thesis,
Otto-von-Guericke University Magdeburg, Germany, January 2018.

Student Supervision

SIMD-Acceleration in SliceList Appenders and Efficient Search in Slices

Individual project of Marten Wallewein-Eising,
2018 (running)


Details Code


Self-Managed Bounded Thread Configuration for Hardware-Efficient String Dictionaries

Individual project of Johannes Wuensche,
2018 (running)


Details Code


Self-Managed Scaling of Hardware-Efficient String Dictionaries

Individual project of Johann Wagner,
2018 (running)


Details Code


Thread Management for High Performance Database Systems

Team project of Robert Jendersie, Johannes Wuensche, Johann Wagner, Marten Wallewein-Eising,
2018


Details Code


Extension of GeckoScript with Conditional Execution and Looping

Scientific Team Project of Andrey Kharitonov, Alishiba D'Souza, Neetha Jambigi and Tirtha Chanda
2018


Details Code


A Survey on Benchmarking Hybrid OLTP/OLAP Database Systems

Seminar of Khaled Abo Alkhir
2017


Details Code


A Survey on Graph Indexing Techniques

Individual project of Anusha Sirigeri
2016


Details Code


Timeline

  • since 2016

    Otto-von-Guericke University, Magdeburg

    Full-Time Research Associate Database Systems

  • 2014-2016

    Otto-von-Guericke University, Magdeburg

    Computer Science, Master of Science

  • 2014-2016

    Otto-von-Guericke University, Magdeburg

    Software Engineer

  • 2016

    METOP GmbH, Magdeburg

    Software Engineer and Research Associate

  • 2011-2014

    Philipps University, Marburg

    Computer Science, Bachelor of Science

  • 2013-2014

    Philipps University, Marburg

    Software Engineer

  • 2012-2014

    Philipps University, Marburg

    Teaching Support "Technical Computer Science" and "Databases"

  • 2012-2013

    Justus-Liebig-Universität, Gießen

    Software Engineer, Architecting and Consulting

  • 2011-2012

    Technical University of Munich, Gießen

    Software Engineer and Architect

  • 2007-2010

    Kolleg und Abendgymnasium Halle/Saale, Halle

    Abitur (Night School)

  • 2008-2009

    Dell Deutschland GmbH, Halle

    Internal Sales Representative

  • 2006-2008

    Deutsche Telekom AG, Halle/Leipzig

    Account-Manager and Expert

  • 2003-2006

    Deutsche Telekom AG, Halle

    IT Systems Engineer

Awards

Best Student Paper Award BTW, 2015

More


MLP MINT Stipendium "Unternehmensinformatik", 2014

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Imprint

Angaben gemäß § 5 TMG

Marcus Pinnecke
Gustav-Adolf-Straße 15
39106 Magdeburg

Vertreten durch:
Marcus Pinnecke

Kontakt:
Telefon: 0391 -67-52845
Fax: 0391 - 67-12020
E-Mail: pinnecke@ovgu.de

Verantwortlich für den Inhalt nach § 55 Abs. 2 RStV:
Marcus Pinnecke
Gustav-Adolf-Straße 15
39106 Magdeburg

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Der Nutzung von im Rahmen der Impressumspflicht veröffentlichten Kontaktdaten durch Dritte zur Übersendung von nicht ausdrücklich angeforderter Werbung und Informationsmaterialien wird hiermit ausdrücklich widersprochen. Die Betreiber der Seiten behalten sich ausdrücklich rechtliche Schritte im Falle der unverlangten Zusendung von Werbeinformationen, etwa durch Spam-Mails, vor.


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Diese Website benutzt Google Analytics, einen Webanalysedienst der Google Inc. (''Google''). Google Analytics verwendet sog. ''Cookies'', Textdateien, die auf Ihrem Computer gespeichert werden und die eine Analyse der Benutzung der Website durch Sie ermöglicht. Die durch den Cookie erzeugten Informationen über Ihre Benutzung dieser Website (einschließlich Ihrer IP-Adresse) wird an einen Server von Google in den USA übertragen und dort gespeichert. Google wird diese Informationen benutzen, um Ihre Nutzung der Website auszuwerten, um Reports über die Websiteaktivitäten für die Websitebetreiber zusammenzustellen und um weitere mit der Websitenutzung und der Internetnutzung verbundene Dienstleistungen zu erbringen. Auch wird Google diese Informationen gegebenenfalls an Dritte übertragen, sofern dies gesetzlich vorgeschrieben oder soweit Dritte diese Daten im Auftrag von Google verarbeiten. Google wird in keinem Fall Ihre IP-Adresse mit anderen Daten der Google in Verbindung bringen. Sie können die Installation der Cookies durch eine entsprechende Einstellung Ihrer Browser Software verhindern; wir weisen Sie jedoch darauf hin, dass Sie in diesem Fall gegebenenfalls nicht sämtliche Funktionen dieser Website voll umfänglich nutzen können. Durch die Nutzung dieser Website erklären Sie sich mit der Bearbeitung der über Sie erhobenen Daten durch Google in der zuvor beschriebenen Art und Weise und zu dem zuvor benannten Zweck einverstanden.

Google AdSense

Diese Website benutzt Google Adsense, einen Webanzeigendienst der Google Inc., USA (''Google''). Google Adsense verwendet sog. ''Cookies'' (Textdateien), die auf Ihrem Computer gespeichert werden und die eine Analyse der Benutzung der Website durch Sie ermöglicht. Google Adsense verwendet auch sog. ''Web Beacons'' (kleine unsichtbare Grafiken) zur Sammlung von Informationen. Durch die Verwendung des Web Beacons können einfache Aktionen wie der Besucherverkehr auf der Webseite aufgezeichnet und gesammelt werden. Die durch den Cookie und/oder Web Beacon erzeugten Informationen über Ihre Benutzung dieser Website (einschließlich Ihrer IP-Adresse) werden an einen Server von Google in den USA übertragen und dort gespeichert. Google wird diese Informationen benutzen, um Ihre Nutzung der Website im Hinblick auf die Anzeigen auszuwerten, um Reports über die Websiteaktivitäten und Anzeigen für die Websitebetreiber zusammenzustellen und um weitere mit der Websitenutzung und der Internetnutzung verbundene Dienstleistungen zu erbringen. Auch wird Google diese Informationen gegebenenfalls an Dritte übertragen, sofern dies gesetzlich vorgeschrieben oder soweit Dritte diese Daten im Auftrag von Google verarbeiten. Google wird in keinem Fall Ihre IP-Adresse mit anderen Daten der Google in Verbindung bringen. Das Speichern von Cookies auf Ihrer Festplatte und die Anzeige von Web Beacons können Sie verhindern, indem Sie in Ihren Browser-Einstellungen ''keine Cookies akzeptieren'' wählen (Im MS Internet-Explorer unter ''Extras > Internetoptionen > Datenschutz > Einstellung''; im Firefox unter ''Extras > Einstellungen > Datenschutz > Cookies''); wir weisen Sie jedoch darauf hin, dass Sie in diesem Fall gegebenenfalls nicht sämtliche Funktionen dieser Website voll umfänglich nutzen können. Durch die Nutzung dieser Website erklären Sie sich mit der Bearbeitung der über Sie erhobenen Daten durch Google in der zuvor beschriebenen Art und Weise und zu dem zuvor benannten Zweck einverstanden.


Impressum vom Impressum Generator der Kanzlei Hasselbach, Rechtsanwälte für Arbeitsrecht und Familienrecht










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