Measuring and Managing Answer Quality for Online Data-Intensive Services

Online data-intensive services parallelize query execution across distributed software components. Interactive response time is a priority, so online query executions return answers without waiting for slow running components to finish. However, data from these slow components could lead to better answers. We propose Ubora, an approach to measure the effect of slow running components on the quality of answers. Ubora randomly samples online queries and executes them twice. The first execution elides data from slow components and provides fast online answers; the second execution waits for all components to complete. Ubora uses memoization to speed up mature executions by replaying network messages exchanged between components. Our systems-level implementation works for a wide range of platforms, including Hadoop/Yarn, Apache Lucene, the EasyRec Recommendation Engine, and the OpenEphyra question answering system. Ubora computes answer quality much faster than competing approaches that do not use memoization. With Ubora, we show that answer quality can and should be used to guide online admission control. Our adaptive controller processed 37% more queries than a competing controller guided by the rate of timeouts.Comment: Technical Repor

RDF Analytics: Lenses over Semantic Graphs

International audienceThe development of Semantic Web (RDF) brings new requirements for data analytics tools and methods, going beyond querying to semantics-rich analytics through warehouse-style tools. In this work, we fully redesign, from the bottom up, core data analytics concepts and tools in the context of RDF data, leading to the first complete formal framework for warehouse-style RDF analytics. Notably, we define i) analytical schemas tailored to heterogeneous, semantics-rich RDF graph, ii) analytical queries which (beyond relational cubes) allow flexible querying of the data and the schema as well as powerful aggregation and iii) OLAP-style operations. Experiments on a fully-implemented platform demonstrate the practical interest of our approach

Semantic-guided predictive modeling and relational learning within industrial knowledge graphs

The ubiquitous availability of data in today’s manufacturing environments, mainly driven by the extended usage of software and built-in sensing capabilities in automation systems, enables companies to embrace more advanced predictive modeling and analysis in order to optimize processes and usage of equipment. While the potential insight gained from such analysis is high, it often remains untapped, since integration and analysis of data silos from different production domains requires high manual effort and is therefore not economic. Addressing these challenges, digital representations of production equipment, so-called digital twins, have emerged leading the way to semantic interoperability across systems in different domains. From a data modeling point of view, digital twins can be seen as industrial knowledge graphs, which are used as semantic backbone of manufacturing software systems and data analytics. Due to the prevalent historically grown and scattered manufacturing software system landscape that is comprising of numerous proprietary information models, data sources are highly heterogeneous. Therefore, there is an increasing need for semi-automatic support in data modeling, enabling end-user engineers to model their domain and maintain a unified semantic knowledge graph across the company. Once the data modeling and integration is done, further challenges arise, since there has been little research on how knowledge graphs can contribute to the simplification and abstraction of statistical analysis and predictive modeling, especially in manufacturing. In this thesis, new approaches for modeling and maintaining industrial knowledge graphs with focus on the application of statistical models are presented. First, concerning data modeling, we discuss requirements from several existing standard information models and analytic use cases in the manufacturing and automation system domains and derive a fragment of the OWL 2 language that is expressive enough to cover the required semantics for a broad range of use cases. The prototypical implementation enables domain end-users, i.e. engineers, to extend the basis ontology model with intuitive semantics. Furthermore it supports efficient reasoning and constraint checking via translation to rule-based representations. Based on these models, we propose an architecture for the end-user facilitated application of statistical models using ontological concepts and ontology-based data access paradigms. In addition to that we present an approach for domain knowledge-driven preparation of predictive models in terms of feature selection and show how schema-level reasoning in the OWL 2 language can be employed for this task within knowledge graphs of industrial automation systems. A production cycle time prediction model in an example application scenario serves as a proof of concept and demonstrates that axiomatized domain knowledge about features can give competitive performance compared to purely data-driven ones. In the case of high-dimensional data with small sample size, we show that graph kernels of domain ontologies can provide additional information on the degree of variable dependence. Furthermore, a special application of feature selection in graph-structured data is presented and we develop a method that allows to incorporate domain constraints derived from meta-paths in knowledge graphs in a branch-and-bound pattern enumeration algorithm. Lastly, we discuss maintenance of facts in large-scale industrial knowledge graphs focused on latent variable models for the automated population and completion of missing facts. State-of-the art approaches can not deal with time-series data in form of events that naturally occur in industrial applications. Therefore we present an extension of learning knowledge graph embeddings in conjunction with data in form of event logs. Finally, we design several use case scenarios of missing information and evaluate our embedding approach on data coming from a real-world factory environment. We draw the conclusion that industrial knowledge graphs are a powerful tool that can be used by end-users in the manufacturing domain for data modeling and model validation. They are especially suitable in terms of the facilitated application of statistical models in conjunction with background domain knowledge by providing information about features upfront. Furthermore, relational learning approaches showed great potential to semi-automatically infer missing facts and provide recommendations to production operators on how to keep stored facts in synch with the real world

Visual knowledge representation of conceptual semantic networks

This article presents methods of using visual analysis to visually represent large amounts of massive, dynamic, ambiguous data allocated in a repository of learning objects. These methods are based on the semantic representation of these resources. We use a graphical model represented as a semantic graph. The formalization of the semantic graph has been intuitively built to solve a real problem which is browsing and searching for lectures in a vast repository of colleges/courses located at Western Kentucky University1. This study combines Formal Concept Analysis (FCA) with Semantic Factoring to decompose complex, vast concepts into their primitives in order to develop knowledge representation for the HyperManyMedia2 platform. Also, we argue that the most important factor in building the semantic representation is defining the hierarchical structure and the relationships among concepts and subconcepts. In addition, we investigate the association between concepts using Concept Analysis to generate a lattice graph. Our domain is considered as a graph, which represents the integrated ontology of the HyperManyMedia platform. This approach has been implemented and used by online students at WKU3

Semantic Data Management in Data Lakes

In recent years, data lakes emerged as away to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Some approaches propose the linkage of metadata to knowledge graphs based on the Linked Data principles to provide more meaning and semantics to the data in the lake. Such a semantic layer may be utilized not only for data management but also to tackle the problem of data integration from heterogeneous sources, in order to make data access more expressive and interoperable. In this survey, we review recent approaches with a specific focus on the application within data lake systems and scalability to Big Data. We classify the approaches into (i) basic semantic data management, (ii) semantic modeling approaches for enriching metadata in data lakes, and (iii) methods for ontologybased data access. In each category, we cover the main techniques and their background, and compare latest research. Finally, we point out challenges for future work in this research area, which needs a closer integration of Big Data and Semantic Web technologies