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The choice of a good join order plays an important role in the query performance of databases. However, determining the best join order is known to be an NP-hard problem with exponential growth with the number of joins. Because of this, nonlearning approaches to join order optimization have a longer optimization and execution time. In comparison, the models of machine learning, once trained, can construct optimized query plans very quickly. Several efforts have applied machine learning to optimize join order for SQL queries outperforming traditional approaches. In this work, we suggest a reinforcement learning technique for join optimization for SPARQL queries, ReJOOSp. SPARQL queries typically contain a much higher number of joins than SQL queries and so are more difficult to optimize. To evaluate ReJOOSp, we further develop a join order optimizer based on ReJOOSp and integrate it into the Semantic Web DBMS Luposdate3000. The evaluation of ReJOOSp shows its capability to significantly enhance query performance by achieving high-quality execution plans for a substantial portion of queries across synthetic and real-world datasets.

Given a set of software programs, each being labeled either as vulnerable or benign, deep learning technology can be used to automatically build a software vulnerability detector. A challenge in this context is that there are countless equivalent ways to implement a particular functionality in a program. For instance, the naming of variables is often a matter of the personal style of programmers, and thus, the detection of vulnerability patterns in programs is made difficult. Current deep learning approaches to software vulnerability detection rely on the raw text of a program and exploit general natural language processing capabilities to address the problem of dealing with different naming schemes in instances of vulnerability patterns. Relying on natural language processing, and learning how to reveal variable reference structures from the raw text, is often too high a burden, however. Thus, approaches based on deep learning still exhibit problems generating a detector with decent generalization properties due to the naming or, more generally formulated, the vocabulary explosion problem. In this work, we propose techniques to mitigate this problem by making the referential structure of variable references explicit in input representations for deep learning approaches. Evaluation results show that deep learning models based on techniques presented in this article outperform raw text approaches for vulnerability detection. In addition, the new techniques also induce a very small main memory footprint. The efficiency gain of memory usage can be up to four orders of magnitude compared to existing methods as our experiments indicate.

Since data are the gold of modern business, companies put a huge effort into collecting internal and external information, such as process, supply chain, or customer data. To leverage the full potential of gathered information, data have to be free of errors and corruptions. Thus, the impacts of data quality and data validation approaches become more and more relevant. At the same time, the impact of information and communication technologies has been increasing for several years. This leads to increasing energy consumption and the associated emission of climate-damaging gases such as carbon dioxide (CO2). Since these gases cause serious problems (e.g., climate change) and lead to climate targets not being met, it is a major goal for companies to become climate neutral. Our work focuses on quality aspects in smart manufacturing lines and presents a finite automaton to validate an incoming stream of manufacturing data. Through this process, we aim to achieve a sustainable use of manufacturing resources. In the course of this work, we aim to investigate possibilities to implement data validation in resource-saving ways. Our automaton enables the detection of errors in a continuous data stream and reports discrepancies directly. By making inconsistencies visible and annotating affected data sets, we are able to increase the overall data quality. Further, we build up a fast feedback loop, allowing us to quickly intervene and remove sources of interference. Through this fast feedback, we expect a lower consumption of material resources on the one hand because we can intervene in case of error and optimize our processes. On the other hand, our automaton decreases the immaterial resources needed, such as the required energy consumption for data validation, due to more efficient validation steps. We achieve the more efficient validation steps by the already-mentioned automaton structure. Furthermore, we reduce the response time through additional recognition of overtaking data records. In addition, we implement an improved check for complex inconsistencies. Our experimental results show that we are able to significantly reduce memory usage and thus decrease the energy consumption for our data validation task.

Due to increasing digitization, the amount of data in the Internet of Things (IoT) is constantly increasing. In order to be able to process queries efficiently, strategies must, therefore, be found to reduce the transmitted data as much as possible. SPARQL is particularly well-suited to the IoT environment because it can handle various data structures. Due to the flexibility of data structures, however, more data have to be joined again during processing. Therefore, a good join order is crucial as it significantly impacts the number of intermediate results. However, computing the best linking order is an NP-hard problem because the total number of possible linking orders increases exponentially with the number of inputs to be combined. In addition, there are different definitions of optimal join orders. Machine learning uses stochastic methods to achieve good results even with complex problems quickly. Other DBMSs also consider reducing network traffic but neglect the network topology. Network topology is crucial in IoT as devices are not evenly distributed. Therefore, we present new techniques for collaboration between routing, application, and machine learning. Our approach, which pushes the operators as close as possible to the data source, minimizes the produced network traffic by 10%. Additionally, the model can reduce the number of intermediate results by a factor of 100 in comparison to other state-of-the-art approaches.

The Internet of Things is anticipated to connect billions of embedded devices equipped with sensors to perceive their surroundings. Thereby, the state of the real world will be available online and in real-time and can be combined with other data and services in the Internet to realize novel applications such as Smart Cities, Smart Grids, or Smart Healthcare. This requires an open representation of sensor data and scalable search over data from diverse sources including sensors. In this paper we show how the Semantic Web technologies RDF (an open semantic data format) and SPARQL (a query language for RDF-encoded data) can be used to address those challenges. In particular, we describe how prediction models can be employed for scalable sensor search, how these prediction models can be encoded as RDF, and how the models can be queried by means of SPARQL.