Applying Class Distance to Decide Similarity on Information Models for Automated Data Interoperability

In the world of the Internet of Things (IoT), heterogeneous systems and devices need to be connected and exchange data with others. How data exchange can be automatically realized becomes a critical issue. An information model (IM) is frequently adopted and utilized to solve the data interoperability problem. Meanwhile, as IoT systems and devices can have different IMs with different modeling methodologies and formats such as UML, IEC 61360, etc., automated data interoperability based on various IMs is recognized as an urgent problem. In this paper, we propose an approach to automate the data interoperability, i.e. data exchange among similar entities in different IMs. First, similarity scores among entities are calculated based on their syntactic and semantic features. Then, in order to precisely get similar candidates to exchange data, a concept of class distance calculated with a Virtual Distance Graph (VDG) is proposed to narrow down obtained similar properties for data exchange. Through analyzing the results of a case study, the class distance based on VDG can effectively improve the precisions of calculated similar properties. Furthermore, data exchange rules can be generated automatically. The results reveal that the approach of this research can efficiently contribute to resolving the data interoperability problem.

Some Results on Prime and Distinct Prime Distance Labeling of Graphs

Let be the set of all integers. A graph is a prime distance graph if there exists an injective function such that for any two adjacent vertices and , the integer is a prime. So is a prime distance graph if and only if there exists a prime distance labeling of . If the edge labels of are also distinct, then is called a distinct prime distance labeling of and is called a distinct prime distance graph. The generalized Petersen graphs are defined to be a graph on vertices with and , subscripts modulo . In this paper, we show that the generalized Petersen graphs permit a prime distance labeling for all even and conjecture that and admit a prime distance labeling for any and all odd , respectively. We also prove that the cycle admits a distinct prime distance labeling for all , besides establishing the prime distance labeling for some graphs.

A Sequence Distance Graph framework for genome assembly and analysis

The Sequence Distance Graph (SDG) framework works with genome assembly graphs and raw data from paired, linked and long reads. It includes a simple deBruijn graph module, and can import graphs using the graphical fragment assembly (GFA) format. It also maps raw reads onto graphs, and provides a Python application programming interface (API) to navigate the graph, access the mapped and raw data and perform interactive or scripted analyses. Its complete workspace can be dumped to and loaded from disk, decoupling mapping from analysis and supporting multi-stage pipelines. We present the design and implementation of the framework, and example analyses scaffolding a short read graph with long reads, and navigating paths in a heterozygous graph for a simulated parent-offspring trio dataset. SDG is freely available under the MIT license at https://github.com/bioinfologics/sdg