Efficient Reachability Ratio Computation for 2-Hop Labeling Scheme

Reachability queries are a fundamental type of queries on graphs that find important applications in numerous domains. Although a plethora of techniques have been proposed for reachability queries, most of them require that the input graph is static, i.e., they are inapplicable to the {\em dynamic} graphs (e.g., social networks and the Semantic Web) commonly encountered in practice. There exist a few techniques that can handle dynamic graphs, but none of them can scale to sizable graphs without significant loss of efficiency. To address this deficiency, this paper presents a novel study on reachability indices for large dynamic graphs. We first introduce a general indexing framework that summarizes a family of reachability indices with the best performance among the existing techniques for static graphs. Then, we propose general and efficient algorithms for handling vertex insertions and deletions under the proposed framework. In addition, we show that our update algorithms can be used to improve the existing reachability techniques on static graphs, and we also propose a new approach for constructing a reachability index from scratch under our framework. We experimentally evaluate our solution on a large set of benchmark datasets, and we demonstrate that our solution not only supports efficient updates on dynamic graphs, but also provides even better query performance than the state-of-the-art techniques for static graphs.

A reachability query is a fundamental graph operation in real graph applications, which answers whether a node can reach another node through a path in a graph. However, the increasingly large amounts of real graph data make it more challenging for query efficiency and scalability. In this paper, we propose a Fruited-Forest (FF) approach to accelerate reachability queries in large graphs by constructing four kinds of fruited-forests from a reduced DAG in different traversal orders. We build different binary-label schemes for the four kinds of fruited-forests to cover reachability between nodes as much as possible, and create a corresponding index for the deleted edges which are deleted during the construction of fruited-forests. Our experimental results on 18 large real graph datasets show that our FF approach requires less index construct time and a smaller index size, which is more scalable to answer reachability queries compared with other existing works.

Graph reachability is fundamental to a wide range of applications, including XML indexing, geographic navigation, Internet routing, ontology queries based on RDF/OWL, etc. Many applications involve huge graphs and require fast answering of reachability queries. Several reachability labeling methods have been proposed for this purpose. They assign labels to the vertices, such that the reachability between any two vertices may be decided using their labels only. For sparse graphs, 2-hop based reachability labeling schemes answer reachability queries efficiently using relatively small label space. However, the labeling process itself is often too time consuming to be practical for large graphs. In this paper, we propose a novel labeling scheme for sparse graphs. Our scheme ensures that graph reachability queries can be answered in constant time. Furthermore, for sparse graphs, the complexity of the labeling process is almost linear, which makes our algorithm applicable to massive datasets. Analytical and experimental results show that our approach is much more efficient than stateof- the-art approaches. Furthermore, our labeling method also provides an alternative scheme to tradeoff query time for label space, which further benefits applications that use tree-like graphs.

Several applications in areas such as biochemistry, GIS, involve storing and querying large volumes of sequential data stored as path collections. There is a number of interesting queries that can be posed on such data. This work focuses on reachability queries: given a path collection and two nodes v s , v t , determine whether a path from v s to v t exists and identify it. To answer these queries, the path-first search paradigm, which treats paths as first-class citizens, is proposed. To improve the performance of our techniques, two indexing structures that capture the reachability information of paths are introduced. Further, methods for updating a path collection and its indices are discussed. Finally, an extensive experimental evaluation verifies the advantages of our approach.

Continuously Evolving Graphs (CEGs) are graphs whose connectivity constantly changes over time is uniquely important for many domains such as social networks, evolutionary genomics, communication networks etc. In many of these it is often important to keep track of connectivity among the nodes of interest as the underlying structure changes over time. While interval-based indexing has been a popular strategy for testing reach ability in static graphs, it cannot be directly applied in the context of evolving graphs. In this paper, we propose CoUPE (Continuous qUery Processing Engine), which, to our best knowledge, is the first time-efficient framework for answering continuous reach ability queries in evolving graphs. The main idea here is to maintain the indices of the evolving graph and recalculate only a subset of reach ability queries using a novel heuristic to determine the change in the state of queries because for the most recent change in the graph. We make three novel contributions while designing CoUPE. First, we introduce a generic indexing and querying framework for answering continuous queries in large time-evolving graphs. Second, we design a highly efficient, scalable and provably correct algorithm for updating the indices of graph by analyzing the changes happening on the graph. Third, we present a novel heuristic-based technique for identifying which subset of existing queries might get effected because of the most recent edit. This paper also presents a detailed experimental study demonstrating the scalability and efficiency of the processing engine.

Graph Analytics (GA) is a rapidly growing field that holds significant importance in numerous applications that focuses on analysing the complex relations in a network. One of the crucial concepts in GA is Query Reachability which refers to the ability to find a path connecting two nodes (u, v) in graph G. The multiple attributes in reachability query processing play a significant role to discover some interesting patterns. Existing studies did not deal with processing complex queries in attributed graphs. To fulfill some of the challenges, We contributed to this paper with three ideas for solving complex queries, finding paths, and processing multiple queries in parallel and distributed paradigms. Therefore, 1) Proposed Distributed Set Reachability Query in Attributed Networks (DSRQ_AN) algorithm using distributed computing through the Neo4j graph data platform and Cypher Query Language, 2) Finding the path to the given complex queries, and 3) Proposed a Multiple Query Processing in Parallel and Distributed (MQP_PD) algorithm to execute in a parallel environment. This paper assessed the performance of the proposed approaches through a parallel paradigm on Neo4j slaves. Furthermore, the demonstrated results of parallel execution outperform sequential execution for multiple queries on attributed graphs.

Network reachability analysis evaluates the actual connectivity of an IT infrastructure. It can be performed by active network probing or examining a formal model of a target IT infrastructure. The latter approach is preferable as it does not interfere with the normal network behaviour and can be easily used during development and change management phases. In this paper we propose a novel modelling approach based on a geometric representation of device configurations (i.e. the policies) which allows the computation of the reachability analysis using the concept of equivalent firewall. An equivalent firewall is a fictitious device, ideally connected directly to the communication endpoints, that summarizes the network behaviour between them. Our model supports routing, filtering and address translation devices in a computationally effective way. In fact, the experimental results show that the computation of equivalent firewalls is performed in a negligible time and that then the reachability queries are answered in few seconds.

Shortest distance computing in road networks is an essential component in a range of applications. As a well-adopted method, 2-hop labeling assigns each vertex a label and enables the distance computation only by a sort-merge join on the labels. However, few existing 2-hop labeling based proposals consider the spatio-temporal characteristics of dynamic query workloads. To process massive-scale shortest distance query workloads, we propose a Workload-aware Core-Forest label index (WCF) to exploit spatial skew in workloads. In addition, we develop a Reinforcement Learning based Time Interval Partitioning (RL-TIP) that utilizes temporal locality to further improve the query performance. Extensive experiments on real-world data demonstrate that our proposal is capable of achieving a query processing speedup of an order of magnitude with less preprocessing time and space, when compared to the state-of-the-art proposals.

Past R tree studies have focused on the number of nodes visited as a metric of query performance. Since database systems usually include a buffering mechanism, we propose that the number of disk accesses is a more realistic measure of performance. We develop a buffer model to analyze the number of disk accesses required for spatial queries using R trees. The model can be used to evaluate the quality of R tree update operations, such as various node splitting and tree restructuring policies, as measured by query performance on the resulting tree. We use our model to study the performance of three well known R tree packing algorithms. We show that ignoring buffer behavior and using number of nodes accessed as a performance metric can lead to incorrect conclusions, not only quantitatively, but also qualitatively. In addition, we consider the problem of how many levels of the R tree should be pinned in the buffer.

Geospatial Data Warehouse (GDW) is a repository of historical and geospatial data used in the decision-making process. This kind of system manages large volumes of data and supports Spatial On-Line Analytical Processing (SOLAP) queries. The use of NoSQL databases in the construction of GDW is still an unexplored topic, even though NoSQL databases present good performance and high scalability with low-cost hardware. In this context, we seek to identify the level of redundancy of geospatial data in Document-Oriented GDW (DGDW) that reduces the storage cost and increases the performance of SOLAP queries. Using the MongoDB database, we exhaustively define and investigate nine DGDW schemas, which have different levels of geospatial data redundancy in their dimensions. We performed an experimental evaluation of these schemas in a cluster structure, to analyze the data volume and the runtime of seven queries which simulate SOLAP operations in geospatial fields with different levels of redundancy and selectivity. Our experimental results indicate that the normalization of low-selectivity geospatial fields and the denormalization of high-selectivity geospatial fields are good strategies to reduce the storage cost and improve the performance of SOLAP queries. The results of our experimental evaluation are an important contribution because they can be used as a guide for construction of DGDW.

The graphs are proliferating in the era of big data, which leads to a lot of opportunities. The traditional algorithms fail to handle the billion-edge graphs. In this paper, a novel Index-based Incremental Structural Clustering technique is proposed, mainly comprised of two algorithms: CSS-based Index builder and Incremental Structural Clustering. For the given graphs, IISCAN first builds the index using the CSS-tree-based Indexing. Then the second algorithm produces the exact clusters, hubs, and outliers like baseline SCAN. The experimental results on the real-world graphs highlight that the proposed IISCAN improves the performance by 3.4x faster. Also, the index size and construction time of the proposed IISCAN outperforms the state-of-the-art.KeywordsBig dataCache-based indexingGraph analyticsGraph clusteringHadoopIndex-based graph clusteringIISCAN

The increasingly large ensemble size of modern High-Performance Computing (HPC) systems has drastically increased the possibility of failures. Performance under failures and its optimization become timely important issues facing the HPC community. In this study, we propose an analytical model to predict the application performance. The model characterizes the impact of coordinated checkpointing and system failures on application performance, considering all the factors including workload, the number of nodes, failure arrival rate, recovery cost, and checkpointing interval and overhead. Based on the model, we gauge three parameters, the number of compute nodes, checkpointing interval, and the number of spare nodes to conduct a comprehensive study of performance optimization under failures. Performance scalability under failures is also studied to explore the performance improvement space for different parameters. Experimental results from both synthetic and actual system failure logs confirm that the proposed model and optimization methodologies are effective and feasible.

Uncertain data has emerged as a key data type in many applications. New and efficient query processing techniques need to be developed due to the inherent complexity of this new type of data. In this paper, we investigate a special type of query, range queries, on uncertain data. We propose a threshold interval indexing structure that aims to balance different time consuming factors to achieve an optimal overall query performance. We also present a more efficient version of our structure which loads its primary tree into memory for faster processing. Experimental results are presented to justify the efficiency of the proposed query processing technique.

The point process model (PPM) for keyword search is a phonetic event-driven approach that provides a whole-word focused alternative to fast lattice matching techniques. Recent efforts in PPMs have been focused on improved model estimation techniques and efficient search algorithms, but past evaluations have been limited to searching relatively easy scripted corpora for simple unigram queries, preventing comprehensive benchmarking against standard search methods. In this paper, we present techniques for score normalization and the processing of multi-word and out of training query terms as required by the 2006 NIST Spoken Term Detection (STD) evaluation, permitting the first comprehensive benchmark of PPM search technology against state-of-the-art word and phonetic-based search systems. We demonstrate PPM to be the fastest phonetic system while posting accuracies competitive with the best phonetic alternatives. Moreover, index construction time and size are better than any keyword search system entered in the NIST evaluation.

Data series similarity search is an important operation and at the core of several analysis tasks and applications related to data series collections. Despite the fact that data series indexes enable fast similarity search, all existing indexes can only answer queries of a single length (fixed at index construction time), which is a severe limitation. In this work, we propose ULISSE , the first data series index structure designed for answering similarity search queries of variable length. Our contribution is two-fold. First, we introduce a novel representation technique, which effectively and succinctly summarizes multiple sequences of different length (irrespective of Z-normalization). Based on the proposed index, we describe efficient algorithms for approximate and exact similarity search, combining disk based index visits and in-memory sequential scans. We experimentally evaluate our approach using several synthetic and real datasets. The results show that ULISSE is several times (and up to orders of magnitude) more efficient in terms of both space and time cost, when compared to competing approaches.