The increasingly complex design has gained difficulty in conducting the rule compliance checking for the Mechanical, Electrical and Plumbing (MEP) system in the design phase. Useful rule-checking ...systems could contribute to a quicker project delivery time. Currently, an efficient method for checking the logical relationship is still lacking. This study aims to propose an MEP rule checking framework using the subgraph matching technology. First, the MEP components in the BIM model are extracted by utilizing the application programming interface (API), and a graph database is established with point-based and curve-based instances being nodes and relationships, respectively. Second, the graph database is simplified to increase the speed of graph matching. Third, the rules, which regulate how the MEP components should be connected, are represented by a knowledge graph. Finally, rule checking is achieved by comparing the graph database against the knowledge graph, and the critical path in a sub-system is detected by calculating the betweenness centrality. A case study with a rail station is used to evaluate the approach where the overall model checking and rule checking are conducted on the original and simplified graph databases sequentially. The results show that the proposed approach could achieve the rule compliance checking at a high speed, and 6 unconnected instances along with 155 problematic pipe fittings have been found. Besides, the critical path for the selected ACS system is from the water-cooled chiller to the condenser water pump. The proposed framework could help in the overall model checking and rule checking process, improving the efficiency of BIM engineers. This research demonstrates that converting a BIM model into a graph database can benefit conventional BIM analysis methods by incorporating advanced technologies (e.g., artificial intelligence) to enable a more flexible and accurate MEP design process.
Process-oriented data analysis techniques allow organizations to understand how their processes operate, where modifications are needed and where enhancements are possible. A recurrent task in any ...process analysis technique is querying. Process data querying allows analysts to easily explore the data with the intent of getting insights about the execution of business processes. The current generation of process query languages targets data scientists. However, there is a need to a query language to support domain analysts who may be inexperienced with database technologies. This paper addresses this challenge by proposing a natural language interface that assists the end-users in querying the stored event data. The interface takes a natural language query from the user, automatically constructs a corresponding structured query to be executed over the stored event data. We use graph based storage techniques, namely labeled property graphs, which allow to explicitly model event data relationships. As an executable query language, we use the Cypher language which is widely used for querying property graphs. The approach has been implemented and evaluated using two publicly available event logs.
•We proposed a natural language interface for querying process execution data from natural language.•We presents a Labeled Graph metamodel for stroing process data.•We proposed a hybrid pipline to automatically constructing Cypher queries from natural language.•Our NLI system is hybrid and combines machine learning and rule-based approaches.•We defined a set of general intent patterns that are domain-independent.•We evaluated the proposed system with more than 530 natural language queries.
Ontology building can greatly influence the development cycle of an information system and enhance interoperability among its constituent elements. Throughout the projects we have been developing we ...have detected, by studying the current literature, a need to develop an agile method to conceive and mapping ontologies, which allows a quick and effective response to R&D projects. Designing a method for building an ontology, which is integrated and aligned with a systematic development approach, represents a crucial challenge in new approaches to system design and exploitation. Extant proposed methods for building an ontology, especially following agile approaches, have achieved interesting results but lack integration and alignment with a wider-view development framework. Thus, we have defined the first version of a semantic model allowing the alignment with the previously defined information model. Following the best practices for ontology building and based on our previous work on software system development, we now propose a method for designing an ontology, the 4SRS Method for Ontological Design based on the V-Model 4SRS, aligning it with a proven development method. We further demonstrate this approach by applying the proposed method in a real case, to develop an ontology for a choen restricted scope within the domain problem.
In recent years, new data characteristics led to the development of new database management systems named NoSQL. As opposed to the mature Relational databases, design methods for the new databases ...receive little attention and mainly consider the data-related requirements. In this paper, we present methods for designing two types of NoSQL databases – Document and Graph databases – that consider not only the data-related but also functional-related requirements. We empirically evaluate the methods and apply the design methods to two leading database systems. We found that the databases designed with the consideration of functional requirements perform better, with respect to time of execution and database I/O operations, compared to when designed without considering them.
Normal operation of a Mechanical, Electrical, and Plumbing (MEP) system under random and intentional attacks is important to a building. A systematic research framework is proposed to analyze the ...resilience of an MEP system and optimize its design. The resilience magnitude in this research measures the ability of the MEP system to keep standard operation when component failures appear. First, the MEP model in Building Information Modelling (BIM) environment is extracted to a graph database by using Revit API, which represents the complex network of an MEP system. Second, the importance of the components and the resilience of the MEP system is analyzed based on the network theory and topological metrics. Third, the failure simulation is carried out by attacking the node of the system randomly and intentionally. Finally, the genetic algorithm is used to optimize the design of the MEP system by adding new edges. The results show that: (i) the graph database is a good representation of the MEP system, and it can convert the 3D model to a format that can be analyzed by data analysis measures, (ii) the same component in the MEP system could have different importance from different perspectives, (iii) the proposed network is more resilient with bridge ratio index and average path length improved by 6.16% and 40.58%, respectively, and (iv) the proposed intentional attack strategy is more conform to reality, and it can cause more severe results to a system. The research can contribute to the implementation of the resilience design theory in the MEP discipline, and create a bond between the 3D model and data analysis.
Relationships or interactions among entities interactions often have occurrence time. So, temporal graph is becoming a popular model to represent temporal data. Temporal graph is generally much ...larger than corresponding non-temporal graph because an non-temporal edge may have many corresponding temporal edges. It raises challenges for querying temporal graphs. Here, we present HyperBit, a temporal graph store which can answer temporal queries efficiently. HyperBit models temporal labeled graph as a series of updates or log records on graph. Then we design an efficient partition storage for log. Since it is costly to answer temporal queries using logs due to full scan of log, we propose an optimal algorithm to build some snapshots to speedup query processing. So, HyperBit can answer temporal queries by applying log records on the snapshot close to the time in query. HyperBit employs SPARQL instead of a new language to describe temporal queries. Thus, HyperBit can process non-temporal queries on temporal/non-temporal graphs. Extensive experiments show that HyperBit significantly outperforms RDF-3x, Jena-TDB in terms of update speed while it has a compact storage. When querying static graphs, HyperBit also outperforms RDF-3X, Jena-TDB by a wide margin and is on par with TripleBit. For temporal queries, HyperBit can easily handle billion graphs, maintaining linear time growth so that has excellent scalability.
The execution time prediction for query tasks in graph database has become difficult and challenging due to the complexity of query plan and system. It is difficult for Database Administrators (DBA) ...or Database Management System (DBMS) to catch the accurate execution time during and before the execution of a query task. Before executing a query task, predicting its execution time can help the DBA or DBMS to efficiently management in the fields of load management, task scheduling, permission control, progress monitoring, system scale customization, etc. Therefore, accurately and efficiently predicting the execution time for query tasks is a key technology in these fields. In this paper, motivated by the combination of artificial intelligence technologies and graph database theories, we first propose a novel deep learning method to predict the execution time for query tasks in graph database. First, each query plan tree of tasks is encoded into an operation sequence. Second, top-20 features are selected from 68 candidate system features using random forest (RF), and the selected top-20 features are reduced to five principal components using principal component analysis (PCA). Finally, an accurate and efficient model based on the long short-term memory (LSTM) is designed and implemented to predict the execution time. The model can predict the execution time in advance before executing a query task in graph database. The experimental results from six kinds of benchmarks with the public data set Yelp show that the average accuracy of the proposed model can reach 81.34% with a high prediction efficiency rate, which proves the feasibility of the deep learning method. In particular, the proposed model can achieve the state-of-the-art prediction performance for query task execution time.
•A post-order traversal is proposed to encode query plan trees as operation sequences.•Using random forest and principal component analysis to select and reduce features.•A LSTM-based prediction model is used to capture temporal dependencies from features.•Experimental results show that the model achieves the state-of-the-art performance.
A retrospective of knowledge graphs Yan, Jihong; Wang, Chengyu; Cheng, Wenliang ...
Frontiers of Computer Science,
02/2018, Letnik:
12, Številka:
1
Journal Article
Recenzirano
Information on the Internet is fragmented and presented in different data sources, which makes automatic knowledge harvesting and understanding formidable for machines, and even for humans. Knowledge ...graphs have become prevalent in both of industry and academic circles these years, to be one of the most efficient and effective knowledge integration approaches. Techniques for knowledge graph construction can mine information from either structured, semi-structured, or even unstructured data sources, and finally integrate the information into knowledge, represented in a graph. Furthermore, knowledge graph is able to organize information in an easy-to-maintain, easy-to-understand and easy-to-use manner.
In this paper, we give a summarization of techniques for constructing knowledge graphs. We review the existing knowledge graph systems developed by both academia and industry. We discuss in detail about the process of building knowledge graphs, and survey state-of-the-art techniques for automatic knowledge graph checking and expansion via logical inferring and reasoning. We also review the issues of graph data management by introducing the knowledge data models and graph databases, especially from a NoSQL point of view. Finally, we overview current knowledge graph systems and discuss the future research directions.
Object Graph Programming Thimmaiah, Aditya; Lampropoulos, Leonidas; Rossbach, Christopher ...
2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE),
02/2024
Conference Proceeding
Odprti dostop
We introduce Object Graph Programming (OGO), which enables reading and modifying an object graph (i.e., the entire state of the object heap) via declarative queries. OGO models the objects and their ...relations in the heap as an object graph thereby treating the heap as a graph database: each node in the graph is an object (e.g., an instance of a class or an instance of a metadata class) and each edge is a relation between objects (e.g., a field of one object references another object). We leverage Cypher, the most popular query language for graph databases, as OGO's query language. Unlike LINQ, which uses collections (e.g., List) as a source of data, OGO views the entire object graph as a single "collection". OGO is ideal for querying collections (just like LINQ), introspecting the runtime system state (e.g., finding all instances of a given class or accessing fields via reflection), and writing assertions that have access to the entire program state. We prototyped OGO for Java in two ways: (a) by translating an object graph into a Neo4j database on which we run Cypher queries, and (b) by implementing our own in-memory graph query engine that directly queries the object heap. We used OGO to rewrite hundreds of statements in large open-source projects into OGO queries. We report our experience and performance of our prototypes.
