Analyses of manufacturing processes is vital for effective and efficient manufacturing. In complex industrial settings, such analyses should account for data that comes from many different and highly heterogeneous machines, and thus are affected by the data integration challenge. In this work, we show how this challenge can be addressed with semantics using Virtual Knowledge Graphs. For this purpose, we propose the SIB Framework, in which we semantically integrate Bosch manufacturing data. In this demo we we present SIB in action on 2 scenarios for the analysis of the Surface Mounting Process (SMT) pipeline.
electronic control units. The scenario of the demo is the product quality analysis that is performed at the plants and that requires integration of vast amounts of heterogeneous data. More precisely, the demo is focused on failure detection for Surface Mounting Process (SMT) that fundamentally relies on the integration and analysis of data generated by the machines deployed in different phases of the process. Such machines, e.g., for placing electronic components (SMD) and for automated optical inspection (AOI) of solder joints, usually come from different suppliers and they rely on distinct formats and schemata for managing the same data across the process. Hence, the raw, non-integrated data does not give a coherent view of the whole SMT process and hampers analysis of the manufactured products. During the demo the attendees will be able to explore the SMT Ontology we developed, observe sample SMT data, and mappings between the data and ontology. Moreover, we encoded relevant product analysis tasks into a catalog of SPARQL queries formulated over the SMT Ontology. The demo attendees will be able to explore the product analyses tasks, how they were encoded in SPARQL, and how easy such complex tasks can be achieved with the help of SIB. In particular, the latter will be shown by comparing SPARQL queries to the native database queries over the underlying SMT data.
This demo accompanies our accepted in-use track paper at ISWC’20 [3]. 2
Our SIB solution for semantic integration of manufacturing data of the SMT process is depicted in Figure 1. Note that the raw manufacturing log data comes in JSON files generated by various machines and then it is extracted and loaded into a PostgreSQL database. For processing of queries posed over the VKG, SIB relies on the state-of-the-art VKG framework Ontop that computes answers enduser SPARQL queries by translating them into SQL queries, and delegating the execution of the translated SQL queries to the original data sources.
Note that the VKG approach does not require to materialize into a KG all facts entailed by the ontology. Moreover, the workflow of Ontop can be divided into an off-line and an online stage. As the first step at the off-line stage, Ontop loads the OWL 2 QL ontology and classifies it via the built-in reasoner, resulting in a directed acyclic graph stored in memory that represents the complete hierarchy of concepts and that of properties. In the second step, Ontop constructs a so-called saturated mapping, by compiling the concept and property hierarchies into the original VKG mapping. This aspect is important also in SIB, since the domain knowledge encoded in the ontology allows for simplifying the design of the mapping layer. During the offline stage, Ontop also optimizes the saturated mapping by applying structural and semantic query optimization.
During the online stage, Ontop takes a SPARQL query and translates it into
SQL by using the saturated mapping. To do so, it applies a series of
transformations that we briefly summarize here [
We prepared two scenarios for the demo: [S1:] SIB Deployment over Bosch data. In this scenarios the attendees will get a better understanding of the data integration challenge with the Bosch SMT use case and how it can be addressed with the help of semantic technologies offline, prior performing the actual data analyses. In particular, the attendees will be able to look closer at Bosch manufacturing data, to understand particularities of SMD and AOI data formats. Then, the attendees will study the Bosch SMT Ontology by zooming into its classes and properties. Finally, they will be able to study mappings relating the ontology and the data. [S2:] Product analysis with SIB. In this scenario the attendees will be able to benefit from the deployed Bosch VKG solution. In particular, the attendees will study several product analysis tasks for the Bosch SMT use case. Then, they will study how these tasks can be expressed by means of suitable SPARQL queries over the SMT Ontology. Notably, such queries make use of ontology terms to refer to the relevant information assets, and thus are very close to the natural language formulation of the analysis tasks, which in turn makes it easy for Bosch engineers to formulate them. Then, the attendees will experience how to obtain the respective analysis data coming from the process logs, by simply executing such queries over the underlying database via the SIB VKG engine. Finally, the attendees will compare the SPARQL queries and their SQL counterparts to witness how much easier the former are comparing to the latter in terms of the size, number of joins, readability of schema elements.
We now illustrate the data and the queries for the two scenarios. The data is mainly based on two sets of relational tables: SMD Tables whre the most notable ones are smd_event, smd_location, smd_panel, smd_components, and AOI Tables with aoi_event, aoi_location, aoi_panel, and aoi_failures. Consider a sample example record in one of these tables: smd_panel panelId boardNo machineName processedTS location p01 b01 SMD Machine 1 24-04-2020 mes01
We prepared 13 analytical tasks for the demo and they were the result of a collaborative work and a careful selection during two visits to Bosch plants and meetings with Bosch line engineers and line managers. The queries offer a good balance among three dimensions: they are representative for product analyses, offer a good coverage of product analyses tasks, and they are complex enough to account for a reasonable number of domain terms. Consider one such query in natural language and in SPARQL:
Query q3: “Return all panels processed from a given time T up to the detection of a failure.”
Despite the temporal nature of the query it can be realized in SPARQL: 1 SELECT DISTINCT ? panel ?ts ? eventTime 2 WHERE {? panel psmt : pTStamp ?ts . { 3 SELECT ? eventTime 4 WHERE {? eventfailure fsmt : eTStamp ? eventTime . 5 FILTER (? eventTime > ’2018 -06 -01 T00 :06:00.000+02:00 ’^^ xsd: dateTimeStamp )} 6 ORDER BY (? eventTime ) LIMIT 1 } 7 FILTER (? ts > ’2018 -06 -01 T00 :06:00.000+02:00 ’^^ xsd: dateTimeStamp && ?ts < ? eventTime ) } 3. Kalaycı, E.G., González, I.G., Lösch, F., Xiao, G., ul Mehdi, A., Kharlamov, E., Calvanese, D.: Semantic integration of bosch manufacturing data using virtual knowledge graphs. In: Proc. ISWC. (2020) 4. Kharlamov, E., Hovland, D., Skjaeveland, M.G., Bilidas, D., Jiménez-Ruiz, E., Xiao, G., Soylu, A., Lanti, D., Rezk, M., Zheleznyakov, D., Giese, M., Lie, H., Ioannidis, Y., Kotidis, Y., Koubarakis, M., Waaler, A.: Ontology based data access in Statoil.
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