York Sure-Vetter

Diefficiency Metrics: Measuring the Continuous Efficiency of Query Processing Approaches

During empirical evaluations of query processing techniques, metrics like execution time, time for the first answer, and throughput are usually reported. Albeit informative, these metrics are unable to quantify and evaluate the efficiency of a query engine over a certain time period – or diefficiency –, thus hampering the distinction of cutting-edge engines able to exhibit high-performance gradually. We tackle this issue and devise two experimental metrics named dief@t and dief@k, which allow for measuring the diefficiency during an elapsed time period t or while k answers are produced, respectively. The dief@t and dief@k measurement methods rely on the computation of the area under the curve of answer traces, and thus capturing the answer concentration over a time interval. We report experimental results of evaluating the behavior of a generic SPARQL query engine using both metrics. Observed results suggest that dief@t and dief@k are able to measure the performance of SPARQL query engines based on both the amount of answers produced by an engine and the time required to generate these answers.

Querying Large Knowledge Graphs over Triple Pattern Fragments: An Empirical Study

Triple Pattern Fragments (TPFs) are a novel interface for accessing data in knowledge graphs on the web. So far, work on performance evaluation and optimization has focused mainly on SPARQL query execution over TPF servers. However, in order to devise querying techniques that efficiently access large knowledge graphs via TPFs, we need to identify and understand the variables that influence the performance of TPF servers on a fine-grained level. In this work, we assess the performance of TPFs by measuring the response time for different requests and analyze how the requests’ properties, as well as the TPF server configuration, may impact the performance. For this purpose, we developed the Triple Pattern Fragment Profiler to determine the performance of TPF server. The resource is openly available at https://doi.org/10.5281/zenodo.1211621. To this end, we conduct an empirical study over four large knowledge graphs in different server environments and configurations. As part of our analysis, we provide an extensive evaluation of the results and focus on the impact of the variables: triple pattern type, answer cardinality, page size, backend and the environment type on the response time. The results suggest that all variables impact on the measured response time and allow for deriving suggestions for TPF server configurations and query optimization.

Discovering Connotations as Labels for Weakly Supervised Image-Sentence Data

Growth of multimodal content on the web and social media has generated abundant weakly aligned image-sentence pairs. However, it is hard to interpret them directly due to intrinsic intension. In this paper, we aim to annotate such image-sentence pairs with connotations as labels to capture the intrinsic intension. We achieve it with a connotation multimodal embedding model (CMEM) using a novel loss function. Its unique characteristics over previous models include: (i) the exploitation of multimodal data as opposed to only visual information, (ii) robustness to outlier labels in a multi-label scenario and (iii) works effectively with large-scale weakly supervised data. With extensive quantitative evaluation, we exhibit the effectiveness of CMEM for detection of multiple labels over other state-of-the-art approaches. Also, we show that in addition to annotation of image-sentence pairs with connotation labels, byproduct of our model inherently supports cross-modal retrieval i.e. image query - sentence retrieval.

Semantic Technologies: Enabler for Knowledge 4.0

Semantic technologies are a key enabler for Knowledge 4.0. Specifically, knowledge graphs have caused significant practical implications for managing knowledge in the digital economy. While most semantic technologies originate from the vision of representing the existing Web in a machine-processable format, it’s most notable success so far are large cross-domain knowledge graphs. They are created by collaborative human modelling and linking of structured and semi-structured data. So far, they exhibit only little but still very powerful semantics, which have shown benefits for numerous applications. This chapter introduces the latest innovations in modelling knowledge using knowledge graphs and explains how those knowledge graphs enable value creation by making unstructured content, like text documents accessible by machines and humans. Finally, we show how semantic technologies help to make hard- and software components in cyber physical systems interoperable.

Considering Human Factors in the Development of Situation-Aware CEP Applications: New Direction Paper

Stream processing has emerged as the major paradigm to tackle the digital information flood. Within stream processing, complex event processing (CEP) represents a pattern-based approach that enables automated situation detection and real-time monitoring. One deficit of CEP applications is the negligence of the evolution of situations, as only two states are differentiated with regard to the occurrence of situations: detected and not detected. This behavior might lead to critical situations due to a decreased comprehensibility for operators. On the other side situation awareness (SAW) is a recognized psychological model describing the characteristics and mechanisms of operators responsible for complex systems. In this paper, we combine the technological method CEP with the human factors identified in SAW to determine the missing links. In order to bridge the gap, this paper presents an extension of the state-of-the-art situation lifecycle to support SAW to its full extent. Furthermore, requirements for situation-aware CEP applications are derived and a methodology to develop such applications is proposed.

3rd International Workshop on Big Data, Smart Data and Semantic Technologies

The growing technical possibilities to gather and aggregate multi-modal data from sensors, mobile devices, social media, log files, cameras, microphones etc. have resulted in large and complex data sets, which today are known as Big Data, are characterized by high data volume, high variety of the data types and data sources, high velocity of the incoming data and the expected information output (real time requirement) as well as the uncertainty about the veracity of the data, which makes it difficult to process the data using existing data management applications and traditional information technologies. On the other hand, when processed properly, Big Data might carry huge amounts of useful information, which was not accessible before and allow for better-founded, more robust predictions and better decision-making amongst others regarding environmental and energy aspects. That is why new predictive and prescriptive analytic approaches are gaining increasing importance.