Kostas Tzoumas

Kostas Tzoumas
Technische Universität Berlin | TUB · Department of Software Engineering and Theoretical Computer Science

PhD

About

21
Publications
13,225
Reads
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2,110
Citations
Additional affiliations
July 2009 - March 2010
Microsoft
Position
  • Intern
February 2009 - June 2009
University of Maryland, College Park
Position
  • Researcher
September 2007 - September 2011
Aalborg University
Position
  • PhD Student
Education
September 2007 - September 2011
Aalborg University
Field of study
  • Computer Science
September 2002 - September 2007
National Technical University of Athens
Field of study
  • Electrical and Computer Engineering

Publications

Publications (21)
Article
Full-text available
Stream processors are emerging in industry as an apparatus that drives analytical but also mission critical services handling the core of persistent application logic. Thus, apart from scalability and low-latency, a rising system need is first-class support for application state together with strong consistency guarantees, and adaptivity to cluster...
Article
Full-text available
Distributed stateful stream processing enables the deployment and execution of large scale continuous computations in the cloud, targeting both low latency and high throughput. One of the most fundamental challenges of this paradigm is providing processing guarantees under potential failures. Existing approaches rely on periodic global state snapsh...
Conference Paper
Full-text available
Over the past years, parallel dataflow systems have been employed for advanced analytics in the field of data mining where many algorithms are iterative. These systems typically provide fault tolerance by periodically checkpointing the algorithm's state and, in case of failure, restoring a consistent state from a checkpoint. In prior work, we prese...
Patent
This patent application relates to enhanced logical recovery techniques for redo recovery operations of a system with an unbundled storage engine. These techniques can be implemented by utilizing an enhanced logical recovery approach in which a dirty page table (DPT) is constructed based on information logged during normal execution. The unbundled...
Article
Full-text available
Apache Flink 1 is an open-source system for processing streaming and batch data. Flink is built on the philosophy that many classes of data processing applications, including real-time analytics, continuous data pipelines, historic data processing (batch), and iterative algorithms (machine learning, graph analysis) can be expressed and executed as...
Article
Iterative computations are in the core of large-scale graph processing. In these applications, a set of parameters is continuously refined, until a fixed point is reached. Such fixed point iterations often exhibit non-uniform computational behavior, where changes propagate with different speeds throughout the parameter set, making them active or in...
Article
Full-text available
We present Stratosphere, an open-source software stack for parallel data analysis. Stratosphere brings together a unique set of features that allow the expressive, easy, and efficient programming of analytical applications at very large scale. Stratosphere’s features include “in situ” data processing, a declarative query language, treatment of user...
Conference Paper
Full-text available
Executing data-parallel iterative algorithms on large datasets is crucial for many advanced analytical applications in the fields of data mining and machine learning. Current systems for executing iterative tasks in large clusters typically achieve fault tolerance through rollback recovery. The principle behind this pessimistic approach is to perio...
Conference Paper
Full-text available
Iterative algorithms occur in many domains of data analysis, such as machine learning or graph analysis. With increasing interest to run those algorithms on very large data sets, we see a need for new techniques to execute iterations in a massively parallel fashion. In prior work, we have shown how to extend and use a parallel data flow system to e...
Conference Paper
Full-text available
Data flows are a popular abstraction to define data-intensive processing tasks. In order to support a wide range of use cases, many data processing systems feature MapReduce-style user-defined functions (UDFs). In contrast to UDFs as known from relational DBMS, MapReduce-style UDFs have less strict templates. These templates do not alone provide al...
Article
Full-text available
Query optimizers rely on statistical models that succinctly describe the underlying data. Models are used to derive cardinality estimates for intermediate relations, which in turn guide the optimizer to choose the best query execution plan. The quality of the resulting plan is highly dependent on the accuracy of the statistical model that represent...
Article
Full-text available
In many massively parallel data management platforms, programs are represented as small imperative pieces of code connected in a data flow. This popular abstraction makes it hard to apply algebraic reordering techniques employed by relational DBMSs and other systems that use an algebraic programming abstraction. We present a code analysis technique...
Article
Full-text available
The current research focus on Big Data systems calls for a rethinking of data generation methods. The traditional sequential data generation approach is not well suited to large-scale systems as generating a terabyte of data may require days or even weeks depending on the number of constraints imposed on the generated model. We demonstrate Myriad,...
Article
Full-text available
Many systems for big data analytics employ a data flow abstraction to define parallel data processing tasks. In this setting, custom operations expressed as user-defined functions are very common. We address the problem of performing data flow optimization at this level of abstraction, where the semantics of operators are not known. Traditionally,...
Article
Parallel dataflow systems are a central part of most analytic pipelines for big data. The iterative nature of many analysis and machine learning algorithms, however, is still a challenge for current systems. While certain types of bulk iterative algorithms are supported by novel dataflow frameworks, these systems cannot exploit computational depend...
Article
Full-text available
As a result of decades of research and industrial development, modern query optimizers are complex software artifacts. However, the quality of the query plan chosen by an optimizer is largely determined by the quality of the underlying statistical summaries. Small selectivity estimation errors, propagated exponentially, can lead to severely sub-opt...
Article
New hardware platforms, e.g. cloud, multi-core, etc., have led to a reconsideration of database system architecture. Our Deuteronomy project separates transactional functionality from data management functionality, enabling a flexible response to exploiting new platforms. This separation requires, however, that recovery is described logically. In t...
Article
Full-text available
Optimization of join queries based on average selectivities is suboptimal in highly correlated databases. In such databases, relations are naturally divided into partitions, each partition having substantially different statistical characteristics. It is very compelling to discover such data partitions during query optimization and create multiple...
Article
Given a set of users, their friend relationships, and a distance threshold per friend pair, the proximity detection problem is to find each pair of friends such that the Euclidean distance between them is within the given threshold. This problem plays an essential role in friend-locator applications and massively multiplayer online games. Existing...
Article
The increased deployment of sensors and data communication net- works yields data management workloads with update loads that are intense, skewed, and highly bursty. Query loads resulting from location-based services are expected to exhibit similar character- istics. In such environments, index structures can easily become performance bottlenecks....

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Project (1)