Raik Niemann

A hybrid index structure for geo-textual searches

The efficient execution of multi-criteria queries has gained increasing interest over the last years. In the present paper we propose an R-tree based approach for queries addressing textual as well as geographic filter conditions. Whereas most previous approaches use an index structure optimised for a single criterion adding special treatment for the other criterion at the leaf nodes or end points of this index structure, our approach uses a deeper integration. In short, R-trees are maintained for certain subsets of the whole term set. Furthermore, in each of these R-trees bit sets are used within the nodes to indicate whether entries for the terms associated with the single bits can be found in the corresponding sub-tree. Our index structure aims to be both, time and space efficient. The paper investigates the efficiency and applicability of the proposed index structure via practical experiments based on real-world and synthetic data.

Setting Up a Big Data Project: Challenges, Opportunities, Technologies and Optimization

In the first part of this chapter we illustrate how a big data project can be set up and optimized. We explain the general value of big data analytics for the enterprise and how value can be derived by analyzing big data. We go on to introduce the characteristics of big data projects and how such projects can be set up, optimized and managed. Two exemplary real word use cases of big data projects are described at the end of the first part. To be able to choose the optimal big data tools for given requirements, the relevant technologies for handling big data are outlined in the second part of this chapter. This part includes technologies such as NoSQL and NewSQL systems, in-memory databases, analytical platforms and Hadoop based solutions. Finally, the chapter is concluded with an overview over big data benchmarks that allow for performance optimization and evaluation of big data technologies. Especially with the new big data applications, there are requirements that make the platforms more complex and more heterogeneous. The relevant benchmarks designed for big data technologies are categorized in the last part.

Evaluating the Energy Efficiency of OLTP Operations

With the continuous increase of online services as well as energy costs, energy consumption becomes a significant cost factor for the evaluation of data center operations. A significant contributor to that is the performance of database servers which are found to constitute the backbone of online services. From a software approach, while a set of novel data management technologies appear in the market e.g. key-value based or in-memory databases, classic relational database management systems (RDBMS) are still widely used. In addition from a hardware perspective, the majority of database servers is still using standard magnetic hard drives (HDDs) instead of solid state drives (SSDs) due to lower cost of storage per gigabyte, disregarding the performance boost that might be given due to high cost.

Benchmarking the Availability and Fault Tolerance of Cassandra

To be able to handle big data workloads, modern NoSQL database management systems like Cassandra are designed to scale well over multiple machines. However, with each additional machine in a cluster, the likelihood for hardware failure increases. In order to still achieve high availability and fault tolerance, the data needs to be replicated within the cluster. Predictable and stable response times are required by many applications even in the case of a node failure. While Cassandra guarantees high availability, the influence of a node failure on the system performance is still unclear.