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MotivationIn the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy.ResultsHere, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named “Neo4COVID19” is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.Availabilityhttps://neo4covid19.ncats.io

Over the last few decades, data has assumed a central role, becoming one of the most valuable items in society. The exponential increase of several dimensions of data, e.g. volume, velocity, variety, veracity, and value, has led the definition of novel methodologies and techniques to represent, manage, and analyse data. In this context, many efforts have been devoted in data reuse and integration processes based on the semantic web approach. According to this vision, people are encouraged to share their data using standard common formats to allow more accurate interconnection and integration processes. In this article, the authors propose an ontology matching framework using novel combinations of semantic matching techniques to find accurate mappings between formal ontologies schemas. Moreover, an upper-level ontology is used as a semantic bridge. An implementation of the proposed framework is able to retrieve, match, and align ontologies. The framework has been evaluated with the state-of-the-art ontologies in the domain of cultural heritage and its performances have been measured by means of standard measures.

Motivation Drug discovery investigations need to incorporate network pharmacology concepts while navigating the complex landscape of drug-target and target-target interactions. This task requires solutions that integrate high-quality biomedical data, combined with analytic and predictive workflows as well as efficient visualization. SmartGraph is an innovative platform that utilizes state-of-the-art technologies such as a Neo4j graph-database, Angular web framework, RxJS asynchronous event library and D3 visualization to accomplish these goals. Results The SmartGraph framework integrates high quality bioactivity data and biological pathway information resulting in a knowledgebase comprised of 420,526 unique compound-target interactions defined between 271,098 unique compounds and 2018 targets. SmartGraph then performs bioactivity predictions based on the 63,783 Bemis-Murcko scaffolds extracted from these compounds. Through several use-cases, we illustrate the use of SmartGraph to generate hypotheses for elucidating mechanism-of-action, drug-repurposing and off-target prediction. Availability https://smartgraph.ncats.io/.

With the rapid development of knowledge graph related technologies, domain knowledge graph has become a research hotspot in academia and industry. However, the domain knowledge graph for technical documents is not mature enough, and the semantic information implicit in unstructured technical documents has not been fully tapped. Combining the characteristics of technical documents, the paper proposes a TextCNN-based topic information extraction model and constructs a domain knowledge graph for technical documents. It uses the graph database Neo4j for knowledge storage and visualization. The information extraction model based on TextCNN can automatically extract the subject information of the document and the summary information such as title, ID, status, meeting, organization, etc. Experiments show that the model has high accuracy on the technical document dataset, which can effectively reduce the cost of manual annotation and data collation. At the same time, knowledge graph visualization can facilitate scientific researchers to search, track and update technical documents, which can show the evolution of technology more clearly.

With the development of a new generation of information technology, such as big data and cognitive intelligence, we are in the postmodern era of artificial intelligence. Currently, the manufacturing industry is in the critical period of transitioning to smart manufacturing, but the cognitive capabilities of devices in smart factories are still scarce. Knowledge Graph (KG) is one of the key technologies of cognitive intelligence, which opens a new path for the horizontal integration of intelligent manufacturing. Therefore, this paper proposes and builds a manufacturing equipment information query system based on KG. Firstly, a large amount of heterogeneous data that contains vast devices information is obtained from the network. Secondly, the conditional random fields (CRF) algorithm is used to extract the entity name, product place, and company name of the device, and then the relationship between the device entities is identified by calculating the similarity and Chinese syntax analysis. In the validation section, we use to the map of Neo4j graph database, when we input a name of a device in the search box, the system can return a relational graph node. In addition, the shortest path optimization algorithm is used to calculate the similarity between nodes in the search process to achieve the recommendation of similar node information.

Objective Overconsumption of processed foods has led to an increase in chronic diet-related diseases such obesity and type 2 diabetes. Although diets high in fresh fruits and vegetables are linked with healthier outcomes, the specific mechanisms for these relationships are poorly understood. Experiments examining plant phytochemical production and breeding programs, or separately on the health effects of nutritional supplements have yielded results that are sparse, siloed, and difficult to integrate between the domains of human health and agriculture. To connect plant products to health outcomes through their molecular mechanism an integrated computational resource is necessary. Results We created the Aliment to Bodily Condition Knowledgebase (ABCkb) to connect plants to human health by creating a stepwise path from plant $$\rightarrow$$ → plant product $$\rightarrow$$ → human gene $$\rightarrow$$ → pathways $$\rightarrow$$ → indication. ABCkb integrates 11 curated sources as well as relationships mined from Medline abstracts by loading into a graph database which is deployed via a Docker container. This new resource, provided in a queryable container with a user-friendly interface connects plant products with human health outcomes for generating nutritive hypotheses. All scripts used are available on github (https://github.com/atrautm1/ABCkb) along with basic directions for building the knowledgebase and a browsable interface is available (https://abckb.charlotte.edu).

Jiajing Wu1,2 1School of Information Science and Engineering, Ocean University of China, Qingdao, 265100,People’s Republic of China; 2School of Mathematics and Computer Science, Chifeng University, Chifeng City, 024000, Inner Mongolia, People’s Republic of ChinaCorrespondence: Jiajing Wu Email wujiajing@stu.ouc.edu.cnAbstract: Since first outbreak of respiratory disease in China, the Coronavirus epidemic (COVID-19) spread on a large scale, causing huge losses to individuals, families, communities and society in the country. The conventional research on the transmission process is basically to study the law or trend of the transmission of infectious diseases from a macro perspective. For in-depth study of the critical data of the newly confirmed patients, one effective way to improve the social isolation measures requires the formation of an organized tracking knowledge system for the confirmed patients and the personnel who have been removed, and the deep data mining and application. Knowledge graph (KG) is one of the irreplaceable techniques to quickly gather patient contact information and outbreak event, which reflecting the relationship between knowledge evolution and structure of novel Coronavirus. Therefore, this paper proposes a method for the analysis of COVID-19 epidemic situation using knowledge graph combined with interactive visual analysis. Firstly, based on the key factors of novel Coronavirus disease, the entity model of the patient, the relationship type of the patient and the expression of knowledge modeling were proposed, and the knowledge graph of the action track of the COVID-19 patient was deeply explored and comparative summarized. Secondly, in the process of constructing knowledge graph, conditional random field (CRF) algorithm is used to extract entity and knowledge. Meanwhile, to better analyze the disease relationship between patients, the semantic relationship of knowledge graph was combined with the visualization of knowledge graph, and the semantic model was verified by deep learning calculation and node attribute similarity. To discover the community detection of patients in the patient knowledge graph, this paper uses PageRank combined with Label propagation algorithms to discover community propagation in the network. Finally, COVID-19 epidemic situation was analyzed from confirmed patient data and multi-view collaborative interactions, such as map distribution visualization, knowledge graph visualization, and track visualization. The results show that the construction of a knowledge graph of COVID-19 patient activity is feasible for the transmission process, analysis of key nodes and tracing of activity tracks.Keywords: COVID-19, knowledge graph, Neo4j, CRF, tracking

Considering that the existing domain knowledge graphs have difficulty in updating data in a timely manner and cannot make use of knowledge sufficiently in the construction process, this paper proposes a legal domain knowledge graph construction approach based on 'China Judgments Online' in order to manage the cases' knowledge contained in it. The construction process is divided into two steps. First, we extract the classification relationships of the cases from structured data. Then, we obtain attribute knowledge of cases from semi-structured data and unstructured data through a relationship extraction model based on an improved cross-entropy loss function. The triples describing knowledge of cases are stored through Neo4j. The accuracy of the proposed approach is verified through experiments and we construct a legal domain knowledge graph which contains more than 4K classification relationships and 12K attribute knowledge to prove its validity.

Graph databases are becoming increasingly popular for modeling different kinds of networks for data analysis. They are built over the property graph data model, where nodes and edges are annotated with property-value pairs. Most existing work in the field is based on graphs were the temporal dimension is not considered. However, time is present in most real-world problems. Many different kinds of changes may occur in a graph as the world it represents evolves across time. For instance, edges, nodes, and properties can be added and/or deleted, and property values can be updated. This paper addresses the problem of modeling, storing, and querying temporal property graphs, allowing keeping the history of a graph database. This paper introduces a temporal graph data model, where nodes and relationships contain attributes (prop-erties) timestamped with a validity interval. Graphs in this model can be heterogeneous, that is, relationships may be of different kinds. Associated with the model, a high-level graph query language, denoted T-GQL, is presented, together with a collection of algorithms for computing different kinds of temporal paths in a graph, capturing different temporal path semantics. T-GQL can express queries like "Give me the friends of the friends of Mary, who lived in Brussels at the same time than her, and also give me the periods when this hap-pened". As a proof-of-concept, a Neo4j-based implementation of the above is also presented, and a client-side interface allows submitting queries in T-GQL to a Neo4j server. Finally, experiments were carried out over synthetic and real-world data sets, with a twofold goal: on the one hand, to show the plausibility of the approach; on the other hand, to analyze the factors that affect performance, like the length of the paths mentioned in the query, and the size of the graph.

Keeping an architecture description of a complex system consistent is difficult. View content is specified by viewpoints which draw upon an underlying metamodel. Viewpoints need to define consistency rules. This becomes increasingly hard as the metamodel size and the number of viewpoints increase. Use of an architecture description language (ADL) such as the Unified Modeling Language (UML) complicates this further because its metamodel may not be able to implement the triples in the AD metamodel and therefore one or more views requiring use of an additional ADL. Even for an architecture framework with a small footprint, such as TRAK, it is a time‐consuming and error‐prone process to keep the metamodel, viewpoint definitions, and implementation consistent. To tackle these TRAK has been modeled by creating views against five viewpoints. These viewpoints can be used to define a metamodel, define the viewpoints for an architecture framework, assess the implementation using one or more ADLs, and record model changes over time. The viewpoint definitions and views are wholly graph‐based and provide a mechanism to define allowed and minimum view content. Views and viewpoints can be read as simple assertions requiring no technical knowledge and support query and analysis. The viewpoint definitions have been validated against a model of TRAK held in a Neo4J graph database. This showed that the TRAK metamodel contains c 750 triples not 500 as had been previously thought. It also identified metamodel elements missing from one of the UML profiles. Example views are provided together with CYPHER queries.

© 2013 IEEE. Although source code programs are commonly written as textual information, they enclose syntactic and semantic information that is usually represented as graphs. This information is used for many different purposes, such as static program analysis, advanced code search, coding guideline checking, software metrics computation, and extraction of semantic and syntactic information to create predictive models. Most of the existing systems that provide these kinds of services are designed ad hoc for the particular purpose they are aimed at. For this reason, we created ProgQuery, a platform to allow users to write their own Java program analyses in a declarative fashion, using graph representations. We modify the Java compiler to compute seven syntactic and semantic representations, and store them in a Neo4j graph database. Such representations are overlaid, meaning that syntactic and semantic nodes of the different graphs are interconnected to allow combining different kinds of information in the queries/analyses. We evaluate ProgQuery and compare it to the related systems. Our platform outperforms the other systems in analysis time, and scales better to program sizes and analysis complexity. Moreover, the queries coded show that ProgQuery is more expressive than the other approaches. The additional information stored by ProgQuery increases the database size and associated insertion time, but these increases are significantly lower than the query/analysis performance gains obtained.

Detecting and analyzing patient insights from social media enables healthcare givers to better understand what patients want and also to identify their pain points. Healthcare institutions cannot neglect the need to monitor and analyze popular social media outlets such as Twitter and Facebook. To have a study success, a healthcare giver needs to be able to engage with their patients and adapt to their preferences effectively. However, data-driven decision-making is no longer enough, as the best-in-class organizations struggle to realize tangible benefits from their data-driven analytics investments. Relying on simplistic textual analytics that use big data technologies to learn consumer/patient insights is no longer sufficient as most of these analytics utilize sort of bag-of-words counting algorithms. The majority of projects utilizing big data analytics have failed due to the obsession with metrics at the expense of capturing the customer's perspective data, as well as the failure in turning consumer insights into actions. Most of the consumer insights can be captured with qualitative research methods that work with small, even statistically insignificant, sample sizes. Employing qualitative analytics provide some kind of actionable intelligence which acquires understanding to broad questions about the consumer needs in tandem with analytical power. Generating insight, on one hand, requires sound techniques to measure consumers' engagement more precisely and offers depth analytics to the consumer data story. On the other hand, turning relevant insights into actions requires incorporating actionable intelligence across the business by verify hypotheses based on qualitative findings by using web analytics to see if these axioms apply to a large number of customers. The first component of our visionary approach is dedicated to identifying the relationships between constituents of the healthcare pain points as echoed by the social media conversation in terms of sociographic network where the elements composing these conversations are described as nodes and their interactions as links. In this part, conversation groups of nodes that are heavily connected will be identified representing what we call conversation communities. By identifying these conversation communities several consumer hidden insights can be inferred from using techniques such as visualizing conversation graphs relevant to given pain point, conversation learning from question answering, conversations summaries, conversation timelines, conversation anomalies and other conversation pattern learning techniques. These techniques will identify and learn the patient insights without forgetting from the context of conversation communities, are tagged as “thick data analytics”. Additionally machine learning methods can be used as assistive techniques to learn from the identified thick data and build models around identified thick data. With the use of transfer learning we also can fine tune these models with the arrival of new conversations. The author is currently experimenting with these seven insights driven learning methods described in this paper with massive geo-located Twitter data to infer the quality of care related to the current COVID-19 outbreak.

In recent years, graph databases have become far more important. They have been proven to be an excellent choice for storing and managing large amounts of interconnected data. Since graph databases (GDB) rely on a graph data model based on graph theory, this study examines whether currently available graph database management systems support the principles of graph theory, and, if so, to what extent. We also show how these systems differ in terms of implementation and languages, and we also discuss which graph database management systems are used today and why.

Kako smo unatrag nekoliko godina dobili određena sredstva (tzv. sveučilišne potpore) kojima smo istraživali određene aspekte grafovskih baza podataka, odlučili smo rezultate istraživanja predstaviti i hrvatskoj akademskoj i stručnoj zajednici. Smatramo kako su istraživanja bila kvalitetna i da rezultati istih imaju potencijal za širu primjenu i implementaciju u dostupnim sustavima za upravljanje grafovskim bazama podataka. Također, postoje i određene mogućnosti za proširenje implementiranih rješenja, kao i prostor za dodatna istraživanja i preslikavanja relacijskih u koncepte grafovskih baza podataka., We received certain funds (so-called University grants) a few years ago to explore certain aspects of graph databases. In this study, we decided to share the results of the research to the Croatian academic and professional community. We believe that the research was of high quality and that the results have the potential for wider application and implementation in the available graph database management systems. In addition, there are certain possibilities for expanding the implemented solutions, and there is space for additional research and mapping of relational into graph database concepts.

Since 1970, relational database models have been in use for storing, manipulating and retrieving data. The importance of relational databases is going to decrease due to the exponential growth of data as it is difficult to work with large number of joining tables. For such kinds of problems, one of the best solutions is to use graph database for storing data. The graph database can be used to store highly connected data. In this article, we are going to put forward a comparison between relational database and graph database with reference to an experiment performed.

The study answers the question which database is the best choice for efficient data storage of UML models. Three products were considered: MongoDB, PostgreSQL and Neo4J. The effectiveness test consists of measurement the response time of queries that save and load data. This study also take into account the memory increase ratio during data insertion and the level of complexity of the implementation of the test data mappers used in database queries.

Provision of an uniform query interface facade to access the health-care data present in multiple data-stores being used autonomously by various departments of an hospital, will empower the health-care community to make better decisions and conclusions. One naive approach for implementation of such system is to use any one popular database to store and process all the data pertaining to all the departments of an hospital. Nonetheless, any single data-model cannot efficiently store and process multitude of data generated by healthcare institutions. For example, not all data fit well into row-column format of traditional relational databases. However, modern NoSQL data-stores allow data to be stored in a form closer to their actual representation and usage. Storage and retrieval of data from disparate data-stores has its challenges and issues like dealing with multiple querying languages, understanding different data modeling techniques and, creation and maintenance of Knowledge Base of Data (KBoD). We have developed an intelligent information integration solution named Polyglot-persistent Healthcare Information System-PolyglotHIS which makes use of cooperating agents enabling health-care professionals to retrieve data from heterogeneous data-stores. PolyglotHIS uses multiple data-stores for storage and processing of the HIS data. The rationale is to select the most appropriate data storage technology that meets the specific requirements of each module of the HIS. Architecture of PolyglotHIS consists primarily of multiple cooperative agents. Capabilities and contents of data-stores are stored and inferred using Datalog, a declarative logic programming language used to store set of facts and rules. Design and working principle of PolyglotHIS is illustrated with the help of a running example. Performance analysis of the implemented system showcase that very less latency has been induced by the system. Refereed/Peer-reviewed

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.

International audience; —In this paper, we present a Linked ecosystem of Web services where both Web services, mashups and users are represented as a multigraph structure. For illustration and experimental purposes, a graph has been constructed , in gathering different data from ProgrammableWeb. The graph is stored in a Neo4J graph database and serves as a repository for real collection of data for achieving services/mashups discovery, selection and recommendation.

Object-oriented database management systems represent a technology in which the concepts of the object-oriented programming paradigm are implemented in databases. The impedance mismatch problem between different technologies (e.g., relational and object-oriented) has represented a challenge for developers and researchers for many years now. With the growing popularity of graph databases, it is important to resolve differences between the concepts of graph theory (used in graph databases) and the object-oriented paradigm. In this paper, we propose an approach for representing graph nodes as objects, and introduce the concept of node inheritance in graph databases, which we demonstrate on a simple showcase.

International audience; Big Data has been described as a four-dimensional model with Volume, Variety, Velocity, and Veracity. In this paper we discuss the potential of a model-driven approach (MDA) to tackle design issues of Big Data taking into account the effect of the four dimensions. Our approach considers NoSQL graph databases. The approach is applied to the case of Neo4j database. Our main contribution is an MDA methodology that enables to tackle the four V's dimensions described above. It consists of two major steps: (i) a forward engineering approach based on MDA as well as a set of transformations rules enabling the development of a conceptual, logical, and physical model for big data encompassing the four V's, (ii) a volume-guided approach supporting the generation of test bases dedicated to performance evaluation. We present an illustrative scenario of our forward engineering approach.

We introduce a new method for storing, modeling, and analyzing power grid data. First, we present an architecture for building the network model for a power grid using the open source graph database Neo4j. Second, we design single- and multi-threading systems for initial energization analysis of the power grid network. We design the shortest path search function and conditional search function based on Neo4j. Finally, we compare the functionality and efficiency of our graph database with a traditional relational database in system initial energization analysis and the shortest path function problems on small to large data sets. The results demonstrate the efficiency and effectiveness of topology modeling and analysis using graph database for a power grid network.

In the field of geoinformation and transportation science, the shortest path is calculated on graph data mostly found in road and transportation networks. This data is often stored in various database systems. Many applications dealing with transportation network require calculation of the shortest path. The objective of this research is to compare the performance of Dijkstra shortest path calculation in PostgreSQL (with pgRouting) and Neo4j graph database for the purpose of determining if there is any difference regarding the speed of the calculation. Benchmarking was done on commodity hardware using OpenStreetMap road network. The first assumption is that Neo4j graph database would be well suited for the shortest path calculation on transportation networks but this does not come without some cost. Memory proved to be an issue in Neo4j setup when dealing with larger transportation networks.

The popularization of big data makes the enterprise need to store more and more data. The data in the enterprise&rsquo, s database must be accessed as fast as possible, but the Relational Database (RDB) has the speed limitation due to the join operation. Many enterprises have changed to use a NoSQL database, which can meet the requirement of fast data access. However, there are more than hundreds of NoSQL databases. It is important to select a suitable NoSQL database for a certain enterprise because this decision will affect the performance of the enterprise operations. In this paper, fifteen categories of NoSQL databases will be introduced to find out the characteristics of every category. Some principles and examples are proposed to choose an appropriate NoSQL database for different industries.

Internet has become so widespread that most popular websites are accessed by hundreds of millions of people on a daily basis. Monolithic architectures, which were frequently used in the past, were mostly composed of traditional relational database management systems, but quickly have become incapable of sustaining high data traffic very common these days. Meanwhile, NoSQL databases have emerged to provide some missing properties in relational databases like the schema-less design, horizontal scaling, and eventual consistency. This paper analyzes and compares the consistency model implementation on five popular NoSQL databases: Redis, Cassandra, MongoDB, Neo4j, and OrientDB. All of which offer at least eventual consistency, and some have the option of supporting strong consistency. However, imposing strong consistency will result in less availability when subject to network partition events.

Part 2: Special Session on Privacy Aware Machine Learning for Health Data Science (PAML 2016); International audience; The usage and application of graph databases is increasing. Many research problems are based on understanding relationships between data entities. This is where graph databases are powerful. Nevertheless, software developers model and think in object-oriented software. Combining both approaches leads to a paradigm mismatch. This mismatch can be addressed by using object graph mappers (OGM). OGM adapt graph databases for object-oriented code, to relieve the developer. Most graph database access frameworks only support table-based result outputs. This defeats one of the strongest purposes of using graph databases. In order to harness both the power of graph databases and object-oriented modeling (e.g. type-safety, inheritance, etc.) we propose an open-source framework with two libraries: (1) renesca, which is a graph database driver providing graph-query-results and change-tracking. (2) renesca-magic, a macro-based ER-modeling domain specific language (DSL). Both were tested in a graph-based application and lead to dramatic improvements in code size (factor 10) and extensibility of the code, with no significant effect on performance.

The value of research containing novel combinations of molecules can be seen in many innovative and award-winning research programs. Despite calls to use innovative approaches to address common diseases, an increasing majority of research funding goes toward "safe" incremental research. Counteracting this trend by nurturing novel and potentially transformative scientific research is challenging, it must be supported in competition with established research programs. Therefore, we propose a tool that helps to resolve the tension between safe but fundable research vs. high-risk but potentially transformational research. It does this by identifying hidden overlapping interest around novel molecular research topics. Specifically, it identifies paths of molecular interactions that connect research topics and hypotheses that would not typically be associated, as the basis for scientific collaboration. Because these collaborations are related to the scientists' present trajectory, they are low risk and can be initiated rapidly. Unlike most incremental steps, these collaborations have the potential for leaps in understanding, as they reposition research for novel disease applications. We demonstrate the use of this tool to identify scientists who could contribute to understanding the cellular role of genes with novel associations with Alzheimer's disease, which have not been thoroughly characterized, in part due to the funding emphasis on established research.

Data and Information Visualization is becoming strategic for the exploration and explanation of large data sets due to the great impact that data have from a human perspective. The visualization is the closer phase to the users within the data life cycle’s phases, thus, an effective, efficient and impressive representation of the analyzed data may result as important as the analytic process itself. In this paper, we present our experiences in importing, querying and visualizing graph databases taking one of the most spread lexical database as case study: WordNet. After having defined a meta-model to translate WordNet entities into nodes and arcs inside a labeled oriented graph, we try to define some criteria to simplify the large-scale visualization of WordNet graph, providing some examples and considerations which arise. Eventually, we suggest a new visualization strategy for WordNet synonyms rings by exploiting the features and concepts behind tag clouds.

International audience; In this paper, we describe a Web services recommendation approach where the services' ecosystem is represented as a heterogeneous multigraph, and edges may have different semantics. The recommendation process relies on clustering techniques to suggest services "of interest" to a user. Our approach has been implemented as a tool called WesReG (Web services Recommendation with Graphs) on top of Neo4j and its cypher query language. We present the system implementation details and present the results of experiments on a collection of real Web services.

Regulations in customs and Excise have an important function in regulating legally every policy and decision making. Thus, the type of rules are made varies according to the hierarchy of the regulator. This regulation is also changing, following policy developments that occured in the higgest government. then, what if this organization has not been able to manage its regulatory archiving? even regulatory changes can not be tracked? So, we need a well-organized system that can accomodate all of the rules and the associated changes and connectedness with other type of regulations. This system will help us to provide convenience to users to search, manage and track the history of changes as well as the relationship between the rules used by the organization. This paper propose the design of an ontologybased semantic network using a graph database that use neo4j 2.3.1 as a solution. In the application that uses the sample data, we found 13 types of nodes that contains 242 child nodes and 22 type of relation that contain 548 relation that connect all the node within 3305ms

V tem diplomskem delu je predstavljen primer sistema za pozicioniranje v zaprtih prostorih z uporabo NFC tehnologije. Znano je, da v zaprtih prostorih signal satelitov navigacijskim napravam ni viden, zato pozicioniranje, kot je npr. preko GPSa, ni mogoče. Delo obsega razvoj več aplikacij, vključno z Android aplikacijo na mobilnem telefonu. Uporabnik s pomočjo aplikacije na mobilnem telefonu prebere enolični identifikator iz NFC nalepke in iz strežnika pridobi trenutni položaj in seznam možnih ciljev. Nato lahko izbere cilj in aplikacija ga usmerja po najkrajši poti do cilja preko vmesnih NFC točk, ki jih na tej poti uporabnik lahko prebere. Na zaslonu telefona se prikazujejo informacije, kot so smer, oddaljenost do naslednje točke, celotna oddaljenost do cilja in trenutna hitrost. V delu so opisani matematični in fizikalni problemi in njihove rešitve. V zadnjem poglavju je bila izdelana statistična analiza, ki pripomore pri ugotavljanju anomalij vpliva močnega magnetnega polja. In this diploma thesis an example of the indoor positioning system using NFC technology is presented. The indoor signal of satellites, such as GPS, is not visible to the navigational devices, so the positioning there is not possible. The thesis covers the development of several applications including Android application for smartphones. The smartphone user reads ID from NFC label and gets his current position and the list of all possible destinations from the server. User can then select one destination and can receive the shortest path from the current position to the destination from the server. Mobile application navigates him between NFC labels on this shortest path until the destination is reached. The thesis also covers some mathematical and physical problems connected with the topic and possible solutions. In the last chapter the statistical analysis that can help find strong magnetic field anomalies is presented.

Because healthcare data comes from multiple, vastly different sources, databases must adopt a range of models to process and store it. A polyglot-persistent framework combines relational, graph, and document data models to accommodate information variety. Refereed/Peer-reviewed

Biodiversity data are well-indexed by taxonomic names. While names reconciliation remains a challenge, there has been tremendous progress in recent years, and integration with available phylogenetic information can support sophisticated analyses for evolutionary questions. However, organisms are also linked to each other by relationships of ecology, geographic proximity, shared habitat, management categories, and other attributes, not yet recorded in a well-structured way. These data are best modeled as a graph, which makes these relationships explicit, and available for reasoning across - just like taxonomic relationships. This would support broad analyses of life on Earth not only from an evolutionary perspective but also across many other axes. This case study will describe how several categories of data are being modeled in the Encyclopedia of Life (EOL) v3 using ontology terms. It will focus on several areas where we anticipate sufficient taxonomic coverage to underlie significant search and analytical power: habitat, distribution, body size and metabolism, and provenance. Habitat and distribution terms are good examples of data terms in well structured hierarchies that could support powerful search. Habitat terms are available from and hierarchically organized in the Environment Ontology (ENVO). Geographic distribution knowledge can often be structured by geographic terms based on verbatim locality text when geocoordinates are not available. Geographic terms are available from several providers, notably Geonames (geonames.org), Marineregions.org and Wikidata. Both habitat and distribution terms can also be connected to simpler and less formalized but commonly used hierarchies like the World Wildlife Fund (WWF) Ecoregions. The hierarchy information made available for habitat and geography by the semantic structure of these ontologies supports searches like "wetland plants of South America," which requires the intersection of taxonomic, geographic, and habitat hierarchies. Body size and metabolism traits interact in a particular use case, illustrating the importance of precision of categorical data terms for informing calculations of quantitative traits. The use case EOL is currently working to support is the parameterization of food web interactions in ecological modelling software. Default or starting values are needed for the content of energy (or carbon) within an organism, and the rate of loss thereof through metabolism. This, plus assimilation efficiency, allows the modeling of carbon flow through the food web. Traits available for estimating carbon content and metabolic rate include various measures of body size, for which conversion factors and formulae are available. For phytoplankton, for instance, size may be reported as cell dimensions, cell volume, cell wet mass, cell dry mass, and/or carbon biomass. For an automated tool to derive parameters from these which are fit for use, the different types of data must all be findable, but the measurement types must be distinguished from one another so the correct conversions are performed for each - all in a machine readable way, so the process can be automated. The need for semantically structured data terms in this case is different, but just as critical to the success of the use case. Future work: Other important structured connections can be made through provenance metadata. These connect taxa and specimens to literature, authors, collectors, wildlife observers and other agents. The Social Media of biodiversity data, rendered explicit, could increase connectivity and communication in the global community - particularly benefitting young researchers in isolated regions without the benefit of professional travel or literature subscriptions. To accomplish this, we must leverage human identifiers such as those made available by Open Researcher And Contributor ID (ORCID) and Wikidata.

International audience; When querying databases, users often wish to express vague concepts, as for instance asking for the cheap hotels. This has been extensively studied in the case of relational databases. In this paper, we propose to study how such useful techniques can be adapted to NoSQL graph databases where the role of fuzziness is crucial. Such databases are indeed among the fastest-growing models for dealing with big data, especially when dealing with network data (e.g., social networks). We consider the Cypher declarative query language proposed for Neo4j which is the current leader on this market, and we present how to express fuzzy queries.

Representing and efficiently querying time-varying social network data is a central challenge that needs to be addressed in order to support a variety of emerging applications that leverage high-resolution records of human activities and interactions from mobile devices and wearable sensors. In order to support the needs of specific applications, as well as general tasks related to data curation, cleaning, linking, post-processing, and data analysis, data models and data stores are needed that afford efficient and scalable querying of the data. In particular, it is important to design solutions that allow rich queries that simultaneously involve the topology of the social network, temporal information on the presence and interactions of individual nodes, and node metadata. Here we introduce a data model for time-varying social network data that can be represented as a property graph in the Neo4j graph database. We use time-varying social network data collected by using wearable sensors and study the performance of real-world queries, pointing to strengths, weaknesses and challenges of the proposed approach.

Multiscale models are commonplace in cancer modeling, where individual models acting on different biological scales are combined within a single, cohesive modeling framework. However, model composition gives rise to challenges in understanding interfaces and interactions between them. Based on specific domain expertise, typically these computational models are developed by separate research groups using different methodologies, programming languages, and parameters. This paper introduces a graph-based model for semantically linking computational cancer models via domain graphs that can help us better understand and explore combinations of models spanning multiple biological scales. We take the data model encoded by TumorML, an XML-based markup language for storing cancer models in online repositories, and transpose its model description elements into a graph-based representation. By taking such an approach, we can link domain models, such as controlled vocabularies, taxonomic schemes, and ontologies, with cancer model descriptions to better understand and explore relationships between models. The union of these graphs creates a connected property graph that links cancer models by categorizations, by computational compatibility, and by semantic interoperability, yielding a framework in which opportunities for exploration and discovery of combinations of models become possible.

Background When modeling in Systems Biology and Systems Medicine, the data is often extensive, complex and heterogeneous. Graphs are a natural way of representing biological networks. Graph databases enable efficient storage and processing of the encoded biological relationships. They furthermore support queries on the structure of biological networks. Results We present the Java-based framework STON (SBGN TO Neo4j). STON imports and translates metabolic, signalling and gene regulatory pathways represented in the Systems Biology Graphical Notation into a graph-oriented format compatible with the Neo4j graph database. Conclusion STON exploits the power of graph databases to store and query complex biological pathways. This advances the possibility of: i) identifying subnetworks in a given pathway; ii) linking networks across different levels of granularity to address difficulties related to incomplete knowledge representation at single level; and iii) identifying common patterns between pathways in the database. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1394-x) contains supplementary material, which is available to authorized users.

Modern Artificial Intelligence (AI) methods can produce a large quantity of accurate and richly described data, in domains such as surveillance or automation. As a result, the need to organize data at a large scale in a semantic structure has arisen for long-term data maintenance and consumption. Ontologies and graph databases have gained popularity as mechanisms to satisfy this need. Ontologies provide the means to formally structure descriptive and semantic relations of a domain. Graph databases allow efficient and well-adapted storage, manipulation, and consumption of these linked data resources. However, at present, there is no a universally defined strategy for building AI-oriented ontologies for the automotive sector. One of the key challenges is the lack of a global standardized vocabulary. Most private initiatives and large open datasets for Advanced Driver Assistance Systems (ADASs) and Autonomous Driving (AD) development include their own definitions of terms, with incompatible taxonomies and structures, thus resulting in a well-known lack of interoperability. This paper presents the Automotive Global Ontology (AGO) as a Knowledge Organization System (KOS) using a graph database (Neo4j). Two different use cases for the AGO domain ontology are presented to showcase its capabilities in terms of semantic labeling and scenario-based testing. The ontology and related material have been made public for their subsequent use by the industry and academic communities.