Architecture for Analysis of Streaming Data

While several attempts have been made to construct a scalable and flexible architecture for analysis of streaming data, no general model to tackle this task exists. Thus, our goal is to build a scalable and maintainable architecture for performing analytics on streaming data. To reach this goal, we introduce a 7-layered architecture consisting of microservices and publish-subscribe software. Our study shows that this architecture yields a good balance between scalability and maintainability due to high cohesion and low coupling of the solution, as well as asynchronous communication between the layers. This architecture can help practitioners to improve their analytic solutions. It is also of interest to academics, as it is a building block for a general architecture for processing streaming data

TypEx : a type based approach to XML stream querying

We consider the topic of query evaluation over semistructured information streams, and XML data streams in particular. Streaming evaluation methods are necessarily eventdriven, which is in tension with high-level query models; in general, the more expressive the query language, the harder it is to translate queries into an event-based implementation with finite resource bounds

Relativistic bias in neutrino cosmologies

Halos and galaxies are tracers of the underlying dark matter structures. While their bias is well understood in the case of a simple Universe composed dominantly of dark matter, the relation becomes more complex in the presence of massive neutrinos. Indeed massive neutrinos introduce rich dynamics in the process of structure formation leading to scale-dependent bias. We study this process from the perspective of general relativity employing a simple spherical collapse model. We find a characteristic signature at the neutrino free-streaming scale in addition to a large-scale feature from general relativity. The scale-dependent halo bias opposes the suppression in the matter distribution due to neutrino free-streaming and leads to corrections of a few percent in the halo power spectrum. It is not only sensitive to the sum of the neutrino-masses, but respond to the individual masses. Accurate models for the neutrino bias are a crucial ingredient for the future data analysis and play an important role in constraining the neutrino masses.Comment: 20 pages, 10 figure

Heuristics Miners for Streaming Event Data

More and more business activities are performed using information systems. These systems produce such huge amounts of event data that existing systems are unable to store and process them. Moreover, few processes are in steady-state and due to changing circumstances processes evolve and systems need to adapt continuously. Since conventional process discovery algorithms have been defined for batch processing, it is difficult to apply them in such evolving environments. Existing algorithms cannot cope with streaming event data and tend to generate unreliable and obsolete results. In this paper, we discuss the peculiarities of dealing with streaming event data in the context of process mining. Subsequently, we present a general framework for defining process mining algorithms in settings where it is impossible to store all events over an extended period or where processes evolve while being analyzed. We show how the Heuristics Miner, one of the most effective process discovery algorithms for practical applications, can be modified using this framework. Different stream-aware versions of the Heuristics Miner are defined and implemented in ProM. Moreover, experimental results on artificial and real logs are reported