View selection over knowledge graphs in triple stores

Knowledge Graphs (KGs) are collections of interconnected and annotated entities that have become powerful assets for data integration, search enhancement, and other industrial applications. Knowledge Graphs such as DBPEDIA may contain billion of triple relations and are intensively queried with millions of queries per day. A prominent approach to enhance query answering on Knowledge Graph databases is View Materialization, ie., the materialization of an appropriate set of computations that will improve query performance. We study the problem of view materialization and propose a view selection methodology for processing query workloads with more than a million queries. Our approach heavily relies on subgraph pattern mining techniques that allow to create efficient summarizations of massive query workloads while also identifying the candidate views for materialization. In the core of our work is the correspondence between the view selection problem to that of Maximizing a Nondecreasing Submodular Set Function Subject to a Knapsack Constraint . The latter leads to a tractable view-selection process for native triple stores that allows a (1 - e ---1 )-approximation of the optimal selection of views. Our experimental evaluation shows that all the steps of the view-selection process are completed in a few minutes, while the corresponding rewritings accelerate 67.68% of the queries in the DBPEDIA query workload. Those queries are executed in 2.19% of their initial time on average.

Random Graph-based Multiple Instance Learning for Structured IoT Smart City Applications

Because of the complex activities involved in IoT networks of a smart city, an important question arises: What are the core activities of the networks as a whole and its basic information flow structure? Identifying and discovering core activities and information flow is a crucial step that can facilitate the analysis. This is the question we are addressing—that is, to identify the core services as a common core substructure despite the probabilistic nature and the diversity of its activities. If this common substructure can be discovered, a systemic analysis and planning can then be performed and key policies related to the community can be developed. Here, a local IoT network can be represented as an attributed graph. From an ensemble of attributed graphs, identifying the common subgraph pattern is then critical in understanding the complexity. We introduce this as the common random subgraph (CRSG) modeling problem, aiming at identifying a subgraph pattern that is the structural “core” that conveys the probabilistically distributed graph characteristics. Given an ensemble of network samples represented as attributed graphs, the method generates a CRSG model that encompasses both structural and statistical characteristics from the related samples while excluding unrelated networks. In generating a CRSG model, our method using a multiple instance learning algorithm transforms an attributed graph (composed of structural elements as edges and their two endpoints) into a “bag” of instances in a vector space. Common structural components across positively labeled graphs are then identified as the common instance patterns among instances across different bags. The structure of the CRSG arises through the combining of common patterns. The probability distribution of the CRSG can then be estimated based on the connections and distributions from the common elements. Experimental results demonstrate that CRSG models are highly expressive in describing typical network characteristics.

Road network link prediction model based on subgraph pattern

Urban road network (referred to as the road network) is a complex and highly sparse network. Link prediction of the urban road network can reasonably predict urban structural changes and assist urban designers in decision-making. In this paper, a new link prediction model ASFC is proposed for the characteristics of the road network. The model first performs network embedding on the road network through road2vec algorithm, and then organically combines the subgraph pattern with the network embedding results and the Katz index together, and then we construct the all-order subgraph feature that includes low-order, medium-order and high-order subgraph features and finally to train the logistic regression classification model for road network link prediction. The experiment compares the performance of the ASFC model and other link prediction models in different countries and different types of urban road networks and the influence of changes in model parameters on prediction accuracy. The results show that ASFC performs well in terms of prediction accuracy and stability.

Detecting Incremental Frequent Subgraph Patterns in IoT Environments

As graph stream data are continuously generated in Internet of Things (IoT) environments, many studies on the detection and analysis of changes in graphs have been conducted. In this paper, we propose a method that incrementally detects frequent subgraph patterns by using frequent subgraph pattern information generated in previous sliding window. To reduce the computation cost for subgraph patterns that occur consecutively in a graph stream, the proposed method determines whether subgraph patterns occur within a sliding window. In addition, subgraph patterns that are more meaningful can be detected by recognizing only the patterns that are connected to each other via edges as one pattern. In order to prove the superiority of the proposed method, various performance evaluations were conducted.