Key issues for a manufacturing data query system based on graph

Manufacturing industry data are distributed, heterogeneous and numerous, resulting in different challenges including the fast, exhaustive and relevant querying of data. In order to provide an innovative answer to this challenge, the authors consider an information retrieval system based on a graph database. In this paper, the authors focus on determining the essential functions to consider in this context. The authors define a three-step methodology using root causes analysis and resolution. This methodology is then applied to a data set and queries representative of an industrial use case. As a result, the authors list four major issues to consider and discuss their potential resolutions.

Cyber-physical systems (CPSs) in the manufacturing domain can be deployed to support monitoring and analysis of production systems of a factory in order to improve, support, or automate processes, such as maintenance or scheduling. When a network of CPS is subject to frequent changes, the semantic interoperability between the CPSs is of special interest in order to avoid manual, tedious, and error-prone information model alignments at runtime. Ontologies are a suitable technology to enable semantic interoperability, as they allow the building of information models that lank machine-readable meaning to information, thus enabling CPSs to mutually understand the shared information. The contribution of this article is twofold. First, we present an ontology building method that is tailored toward the needs of CPSs in the manufacturing domain. For this purpose, we introduce the requirements regarding this method and discuss related research concerning ontology building. The method itself is designed to begin with ontological requirements and to yield a formal ontology. As the reuse of ontologies and other information resources (IRs) is crucial to the success of ontology building projects, we put special emphasis on how to reuse IRs in the CPS domain. Second, we present a reusable set of ontology design patterns that have been developed with the aforementioned method in an industrial use case and illustrate their application in the considered industrial environment. The contribution of this article extends the method introduced, as a postconference paper, by a detailed industrial application. Note to Practitioners-With growing digitalization in industry, the exchange and use of manufacturing-related data are becoming increasingly important to improve, support, or automate processes. Thus, it is necessary to combine information from different data sources that have been designed by different vendors and may, therefore, be heterogeneous in structure and semantics. A system that plans a maintenance worker's daily schedule, for instance, requires information about the status of machines, production plans, and inventory, which resides in other systems, such as programmable logic controllers (PLCs) or databases. When creating such information systems, accessing, searching, and understanding the different data sources is a time-intensive and error-prone procedure due to the heterogeneities of the data sources. Even worse, this procedure has to be repeated for every newly built system and for every newly introduced data source. To allow for eased access, searching, and understanding of these heterogeneous data sources, ontology can be used to integrate all heterogeneous data sources in one schema. This article contributes a method for building such ontologies in the manufacturing domain. Furthermore, a set of ontology design patterns is presented, which can be reused when building ontologies for a domain.

Unstructured medical image query using big data – An epilepsy case study

An alternative database approach for management of SNOMED CT and improved patient data queries

Wireless communication plays an important role in the digitization of industries. A 5G cellular communication system enables several industrial automation use cases. Fifth-generation deployments in industrial use cases have mainly been carried out in the sub-7 GHz frequency range. In this work, we empirically study 5G system performance in the millimeter wavelength (mmW) range for industrial use cases: additive manufacturing processes and precision manufacturing robotics. We carry out an experimental performance evaluation of a commercially available non-public 5G mmW system to assess its latency, reliability and throughput for uplink and downlink data traffic in a real industrial environment. We also investigate the impact of various 5G configurations on 5G performance characteristics with insights from the baseband log information as well as unidirectional latency measurements. Our empirical results indicate that 5G mmW system can achieve low latency with high reliability in both one-way traffic directions. The throughput is observed to be high for line-of-sight (LOS) scenarios, making the use of the 5G mmW system appealing especially for data rate-intensive and time-critical industrial use cases. We also observe that industrial environments with lots of metal and reflective surfaces provide favorable propagation conditions for non-LOS transmissions. Our results indicate that static industrial use cases with low mobility can leverage the performance benefits of 5G mmW systems.

With the diversification of three-dimensional (3D) urban space data sources and data collection methods, the Open Geospatial Consortium published 3D spatial data standards in Geography Markup Language (GML). Among them, IndoorGML is a data model for expressing the topological relationship between indoor spaces with advantages in terms of semantic information storage and navigation applications. Conversion into a suitable binary file must be accomplished to use the IndoorGML documents through integration with other data. This study proposes a method of generating graph database objects from the documents of IndoorGML to promote usability and interoperability. The labeled property graph model is designed to reflect IndoorGML’s multilayered graph concept. Moreover, in the proposed graph model, the geometry of physical indoor spaces is segregated into separate surfaces. All elements from IndoorGML are managed with specific labels in Neo4j, the most dominant graph database. By applying the proposed method, the graph database is constructed using the officially distributed IndoorGML documents of three buildings. The number of generated graph objects is determined to confirm whether the IndoorGML features are accurately converted into graph database objects. Furthermore, the utility of the constructed graph database is verified through two scenario-based routing tests consisting of finding the optimal room with the lowest route cost and allocating indoor spaces for patrolling to outdoor security offices through indoor and outdoor data integration. In conclusion, this study confirms that the utility of IndoorGML can be promoted using a graph database system in data integration, data queries, and pathfinding applications while preserving data topology and interoperability.

A graph database for life cycle inventory using Neo4j

Since the Human Genome Project was completed in 2003, many data scientists have developed algorithms in order to store and query high volumes of genomic data. The most common data storage techniques employed in these algorithms are flat files or relational databases. While sophisticated indexing techniques can accelerate queries, an alternative is to store biological sequence data directly in a way that supports efficient queries. Here we introduce a new algorithm that aims to compress the redundant information and improve the performance of query speed with the help of graphical databases, which have been commercial available since the mid-late 2000s. A graphical database stores information using nodes and relationships (edges). Our approach is to identify subsequences that are common among many sequences, and to store these as "common nodes" in the graphical database. This is accomplished for sequencing data as follows: split the whole sequence into k-mers: if a given k-mer is common to enough sequences, then it is labeled as a common segment; if a k-mer is unique (or common to too few sequences), then it is labeled as a single segment. Thus, common nodes and single nodes are formed from common segments and single segments, respectively. These two kinds of nodes are connected by edges in the graphical database, allowing each original sequences to be reconstructed by following edges in the graph. This graphical database model allows for fast taxonomic queries of 16S rDNA. When queried, the database can first attempt to find common nodes that match the query sequence, and subsequently follow edges to single nodes to refine the search. This approach is analogous to that of "compressive genomics", except that the compression is implicit in the graphical database storage model. Beyond simple sequence queries, this graphical database representation also supports variability analysis, which identifies highly variable vs. conserved regions of 16S sequence. Regions of low variability correspond to common nodes, while regions of high variability correspond to a variety of paths through single nodes. Figure illustrates common and single nodes, and a corresponding plot of variability. Benchmarking of sequence search indicates that query time in graphical databases is significantly faster than in flat files or relational databases. Implementation of graphical databases in genomic data analysis will allow for accelerated search, and may lend itself to other forms of efficient analysis, such as tetramer frequency analysis, which is useful in metagenomic binning.

Big Data is used to store huge volume of both structured and unstructured data which is so large and is hard to process using current / traditional database tools and software technologies. The goal of Big Data Storage Management is to ensure a high level of data quality and availability for business intellect and big data analytics applications. Graph database which is not most popular NoSQL database compare to relational database yet but it is a most powerful NoSQL database which can handle large volume of data in very efficient way. It is very difficult to manage large volume of data using traditional technology. Data retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are available. This paper describe what is big data storage management, dimensions of big data, types of data, what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic structure of graph database, advantages, disadvantages and application area and comparison of various graph database.

Traditional data warehousing has been very successful in helping business enterprises to make intelligent decisions through declarative analysis of large amount of structured data stored in a relational database. However, not all enterprise data naturally fit into a relational model. Within an enterprise, there are huge amount of unstructured data, such as document content, emails, spreadsheets, that do not have a fixed schema, or have a very sparse or loose schema that cannot be effectively modeled using relational model. Yet, like relational data, unstructured data record many useful facts that are equally essential and important to be analyzed by businesses to make intelligent decisions. In this chapter, we propose an XML-enabled RDBMS that uses XML as the underlying logical data model to uniformly represent both well-structured relational data, semi-structured and unstructured data in building an enterprise data warehouse that is able to store and analyze any data regardless of existence of schema or not. We show how XQuery used in SQL/XML as a declarative language to do data query, analysis and transformation over both structured data and unstructured content in the data warehouse. We present the rationale for using XML as the logical data model for unified data warehouse query, XML extended inverted text index to integrate structured data query and context aware full text search for unstructured content so as to support efficient data analysis over large volume of structured and unstructured data. We argue that the technical approach of using XML to unify both structured and unstructured data in a warehouse has the potential to push business intelligence over all enterprise data to a new era.

The data connection of electric power marketing and distribution network is an integrated platform of operation and distribution data based on geographic information platform, which integrates the data of marketing business application system, production management information system and grid geospatial information service platform, and shares the whole face of production data and marketing customer data, so as to achieve the connection of operation and distribution data and business, and realize fault location and electric failure Scope analysis, line loss statistics, business expansion and installation, etc. Graph database has the technical advantages of high efficiency of topological data organization, low redundancy of topological data storage, strong expansibility of topological data model and high efficiency of topological analysis and calculation, which can meet the storage, query and retrieval requirements of large-scale operation and distribution data. After application verification, the results show that the graph database can provide data storage, query and retrieval technology scheme for the operation and distribution of power grid. With the business development of operation and distribution of power grid, there are performance bottlenecks in the existing data storage mode, which seriously restricts the deepening of application. It is necessary to introduce advanced computing technology to achieve breakthrough, and improve the data storage efficiency of the existing data platform The parallel and concurrent ability of data retrieval and query can meet the management requirements of geometric growth of power grid equipment, the analysis requirements of real-time data of power grid, and the concurrent access requirements of largescale growth of users.

The power system is becoming larger and larger, and the operation is more frequent, which puts higher requirements on the real-time performance of analysis and calculation. The graph database is a new type of database that originated from the parallel processing of massive data in the Internet. The data model can visually express the topology of the grid and easily realize parallel traversal query. Firstly, the characteristics of graph database are introduced from the aspects of data model and data query, and the potential advantages of applying it to large-scale power system analysis and calculation are analyzed. Secondly, the design method of power system modeling is presented for satisfying the guidelines of integrity, consistency and efficiency conforming with CIM/E standard. Finally, a parallel power network topology analysis algorithm is implemented based on the graph database model for a provincial grid. The calculation results of the actual large-scale provincial power grid show that the proposed method can significantly improve the topology search efficiency.KeywordsGraph databaseGraph computingPower grid modelingNetwork topologyCIM/E

In this paper, we solve problem of the ineffectiveness of using RDFS/OWL-stored mechanism for large-scale domain ontology. In particular, when the constructed ontology contains a huge amount of entities and semantic relations, it causes difficulties in managing and visualizing the ontological knowledge as well as low performance in data querying. We resolve these issues by using graph database as the storage mechanism for representing the constructed ontology. The approach of using graph database provides the advantages not only in better ontological data management and visualization but also in the higher performance and flexible of knowledge extracting from ontology via cypher querying language.

As the internet is growing day by day, the amount of data being generated is huge. This data includes structured data and unstructured data. The data along with its relationship with other data makes the most powerful and meaningful information. Maximum data exists in the form of the relationship between different or same objects and the noticeable thing is the relationship between the data is more important than the data itself. These relationships are handled efficiently by Relational databases that store data having structures and which have several records. The important point to be noted here is that these Relational database management Systems use tables with normalization concept. If the amount of data in such tables is huge, then handling such a large amount of data with its relationships is a tedious task. Here, Graph Databases come into picture. Entities and their relationships in relational databases will be reflected with nodes and relationships in graph databases. Graph databases provide very simple data model than databases with Online Transaction Processing systems. Graph databases provide features such as transactional integrity and operational availability. This paper introduces the idea of graph database systems in conjunction with Neo4j encompassing its query features, consistency, transactions, availability, and scaling.

Most prior multimodal transport networks have been organized as relational databases with multilayer structures to support transport management and routing; however, database expandability and update efficiency in new networks and timetables are low due to the strict database schemas. This study aimed to develop multimodal transport networks using a graph database that can accommodate efficient updates and extensions, high relation‐based query performance, and flexible integration in multimodal routing. As a case study, a database was constructed for London transport networks, and routing tests were performed under various conditions. The constructed multimodal graph database showed stable performance in processing iterative queries, and efficient multi‐stop routing was particularly enhanced. By applying the proposed framework, databases for multimodal routing can be readily constructed for other regions, while enabling responses to diversified routings, such as personalized routing through integration with various unstructured information, due to the flexible schema of the graph database.