In the context of the IoT, energy is a scarce resource. Since the transmission of data is one of the largest energy consumers, the amount of data must be reduced as much as possible. Modern database systems optimize the queries to reduce execution times. Therefore they also consider reducing the network traffic but neglect the network topology. Network topology is critical because it can consume more energy to send a few pieces of data over a long distance than to transmit a large amount of data to a neighboring device. Therefore, we propose new techniques for cooperation between routing and application layers.

Abstract Modern computer systems can use different types of hardware acceleration to achieve massive performance improvements. Some accelerators like FPGA and dedicated GPU (dGPU) need optimized data structures for the best performance and often use dedicated memory. In contrast, APUs, which are a combination of a CPU and an integrated GPU (iGPU), support shared memory and allow the iGPU to work together with the CPU on pointer-based data structures. First, we develop an approach for dGPU to accelerate queries in libcuckoo and robin-map and when looking at accelerating insert, updates and erase operations in the original libcuckoo using OneAPI on an APU. We evaluate the dGPU against the CPU variants and our dGPU approach adapted for the CPU and also in a hybrid context by using longer keys on the CPU and shorter keys on the dGPU. In comparison with the original libcuckoo algorithm, our dGPU approach achieves a speed-up of 2.1, and in comparison with the robin-map a speed-up of 1.5. For hybrid workloads, our approach is efficient if long keys are processed on the CPU and short keys are processed on the dGPU. By processing a mixture of 20% long keys on the CPU and 80% short keys on dGPU, our hybrid approach has a 40% higher throughput than the CPU only approach. In addition, we develop a hybrid APU approach for insert, update and erase operations in the original libcuckoo structure focusing on shared memory with iGPU accelerated look-ups of the positions for insert, update and erase operations.

The capabilities of quantum computers, such as the number of supported qubits and maximum circuit depth, have grown exponentially in recent years. Commercially relevant applications that take advantage of quantum computing are expected to be available soon. In this paper, we shed light on the possibilities of accelerating database tasks using quantum computing with examples of optimizing queries and transaction schedules and present some open challenges for future studies in the field.

The Second International Workshop on Big Data in Emergent Distributed Environments (BiDEDE) focuses on scalable data management issues in emergent computing environments like (post) cloud and fog/edge/dew computing. All these computing environments aim to smoothly integrate scalable data management and processing into distributed environments, such that communication and computational costs are reduced for higher throughput, lower latencies of applications and extending battery lifetimes of nodes in companion with robust approaches to overcome failures and crashes. While there has been research in these areas for already over one decade, still many open challenges exist because of technology triggers like lightweight virtualization, increasing capabilities of nodes and increasing massive parallelization. This workshop supports lively discussions in these and related areas.

Quantum computers are known to be efficient for solving combinatorial problems like finding optimal schedules for processing transactions in parallel without blocking. We show how Grover’s search algorithm for quantum computers can be applied for finding an optimal transaction schedule via generating code from the problem instance. We compare our approach with existing approaches for traditional computers and quantum annealers in terms of preprocessing, runtime, space and code length complexity. Furthermore, we show by experiments the expected number of optimal solutions of this problem as well as suboptimal ones. With the help of an estimator of the number of solutions, we further speed up our optimizer for optimal and suboptimal transaction schedules.

Sonifications map data to auditory dimensions and offer a new audible experience to their listeners. We propose a sonification of query processing paired with a corresponding visualization both integrated in a web application. In this demonstration we show that the sonification of different types of relational operators generates different sound patterns, which can be recognized and identified by listeners increasing their understanding of the operators' functionality and supports easy remembering of requirements like merge joins work on sorted input. Furthermore, new ways of analyzing query processing are possible with the sonification approach.

Quantum annealers are a special kind of quantum computers for solving optimization problems. In this paper, we investigate the benefits of quantum annealers in the field of transaction synchronization. In particular, we show how transactions using the 2-phase-locking protocol can be optimally distributed to any number of available machines to reduce transaction waiting times. Therefore an instance of the problem will be transformed into a formula that is accepted by quantum annealers. In an experimental evaluation, the runtime on a quantum annealer outperforms the runtime of traditional algorithms to solve combinatorial problems like simulated annealing already for small problem sizes.

The International Workshop on Semantic Big Data (SBD) is already in the fourth year in conjunction with the ACM SIGMOD Conference. By focusing on topics related to Semantic Web and Big Data, it highlights these important research areas to all participants of the SBD workshop and the main conference. Hence, the workshop offers an ideal forum for open discussions and establishment of professional relationships between academic researchers and industrial members of the Semantic Web and database communities.

Managing and querying Semantic Web are important issues for Semantic Web applications. Therefore, we have developed a Semantic Web database system with logically and physically optimized SPARQL engines to manage and query RDF data, named LuposDate. In order to present the functionalities of the LUPOSDATE system and engines, we have developed an online demonstration, which is available at http://www.ifis.uni-luebeck.de/index.php?id=luposdate-demo.

Caching stores the results of previously answered queries in order to answer succeeding queries faster by reusing these results. We propose two different approaches for using caches of XSLT transformed XML data in order to answer queries. The first approach checks whether or not a current query Q can be directly answered from the result of a previously answered query Qi stored in the cache. The new query is otherwise submitted to the source over the network, the answer of the query is determined, transmitted back to the client, and stored in the cache. The second approach determines only the intersection Q-Qi and integrates the result of Q--Qi into the previous results in the cache, which requires applying a numbering scheme for the output of the XSLT stylesheet. We show by experimental results that the second approach can significantly speed up the answering time in comparison to the first approach, but is not significantly slower in few worst cases than the second approach.