Generalizing and Processing Topological Data using Sentence Data Structure

At present, the graph neural network has achieved good results in the semisupervised classification of graph structure data. However, the classification effect is greatly limited in those data without graph structure, incomplete graph structure, or noise. It has no high prediction accuracy and cannot solve the problem of the missing graph structure. Therefore, in this paper, we propose a high-order graph learning attention neural network (HGLAT) for semisupervised classification. First, a graph learning module based on the improved variational graph autoencoder is proposed, which can learn and optimize graph structures for data sets without topological graph structure and data sets with missing topological structure and perform regular constraints on the generated graph structure to make the optimized graph structure more reasonable. Then, in view of the shortcomings of graph attention neural network (GAT) that cannot make full use of the graph high-order topology structure for node classification and graph structure learning, we propose a graph classification module that extends the attention mechanism to high-order neighbors, in which attention decays according to the increase of neighbor order. HGLAT performs joint optimization on the two modules of graph learning and graph classification and performs semisupervised node classification while optimizing the graph structure, which improves the classification performance. On 5 real data sets, by comparing 8 classification methods, the experiment shows that HGLAT has achieved good classification results on both a data set with graph structure and a data set without graph structure.

Graphs play an increasingly important role in various big data applications. However, existing graph data structures cannot simultaneously address the performance bottlenecks caused by the dynamic updates, large scale, and high query complexity of current graphs. This paper proposes a novel data structure for large-scale dynamic graphs called CuckooGraph. It does not require any prior knowledge of the upcoming graphs, and can adaptively resize to the most memory-efficient form while requiring few memory accesses for very fast graph data processing. The key techniques of CuckooGraph include TRANSFORMATION and DENYLIST. TRANSFORMATION fully utilizes the limited memory by designing related data structures that allow flexible space transformations to smoothly expand/tighten the required space depending on the number of incoming items. DENYLIST efficiently handles item insertion failures and further improves processing speed. Our experimental results show that compared with the most competitive solution Spruce, Cuckoo-Graph achieves about 33× higher insertion throughput while requiring only about 68% of the memory space.

The graph model enables a broad range of analysis, thus graph processing is an invaluable tool in data analytics. At the heart of every graph-processing system lies a concurrent graph data structure storing the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, graph-processing systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and low-memory graph mutations. Existing graph structures offer a hard trade-off between read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these trade-offs and enables both fast read-only analytics and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists to achieve the best of both worlds. We compare CSR++ to CSR, adjacency lists from the Boost Graph Library, and LLAMA, a state-of-the-art update-friendly graph structure. In our evaluation, which is based on popular graph-processing algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average), while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates.

The shortest-path problem is an essential component of many applications including Advanced Traveler Information Systems (ATIS), computer networks, etc. A hierarchical routing algorithm decomposes the original graph into a set of fragment graphs and also into a boundary graph which summarizes the fragment graphs. A fully memoized hierarchical routing algorithm pre-computes and stores the shortest-path data structure and the shortest-path-cost data structure for the graph fragments, as well as for the boundary graph. The storage cost of the fully memoized approach can be reduced by a virtual or a hybrid memoization approach, where few or none of the relevant data structures have been pre-computed. This paper achieves two firsts. We use a real graph with 123,000 nodes and 313,000 edges, which is two orders of magnitude larger than the graphs used in related work. Second, the paper analyzes the effect of memoizing individual data structures for the storage overhead and computation time of A/sup */-based hierarchical routing algorithms. Analysis and experiments with the Twin Cities metropolitan road-map show that memoizing the shortest-path-cost data structure for the boundary graph provides the best savings in computation time, for a given amount of storage and a small number of fragments. Memoizing the relevant part of the shortest-path-cost data structure for the fragment graphs provides the next best savings, followed by memoizing the shortest-path data structure for the boundary graph.

This paper introduces data structures as applied to computer graphics. Design criteria for computer graphics data structures are discussed, followed by a comparison of general-purpose and tailored graphic data structures. A general graphic data structure is introduced as an example of a structure meeting the preceding criteria. The L6language is then examined as a tool for implementing the above data structure, and is compared to a few other language systems.

As a non-linear data structure consisting of nodes and edges, the graph data span many different domains. In the real world, applications based on such data structures are always time-sensitive, that is, the value of graph data tends to decrease with time. Furthermore, the application based on spatio-temporal graph is one of the typical representatives of time-sensitive, since the time dimension is an inherent feature of spatio-temporal data. The Distributed Stream Processing Engine (DSPE) seems an excellent choice for the above requirement, which is commonly partitioned and concurrently processed by a number of threads to maximize the throughput. However, it is not feasible to do such mission directly using the traditional DSPE. In this paper, we propose a computational model suitable for handling the spatio-temporal graph in DSPE, by reconstructing the DSPE’s parallel processing slots. Specifically, our proposal includes a general processing framework to deal with the data structure of the spatio-temporal graph, a state information compensation mechanism to ensure the correctness of processing such stateful operation in DSPE, a lightweight summary information calculation method to ensure the performance of the system. Empirical studies on real-world stream applications validate the usefulness of our proposals and prove the considerable advantage of our approaches over state-of-the-art solutions in the literature.

We present a new succinct data structure for graphs that are “tree-like,” in the sense that the number of “additional” edges (w.r.t. a spanning tree) is not too high. Our algorithmic idea is to represent a BFS-spanning tree of the graph with a succinct data structure for trees, and enhance it with additional information that accounts for the non-tree edges. In practical tests, our data structure performs well for graphs containing up to 10% of non-tree edges, reducing the space of a pointer-based representation by a factor of ≈20, while increasing the worst-case running times for the operations by roughly the same factor.

Smart cities emerge in computer science as a topic to cover how the technology of information and communication can be used in the urban centers to monitor its dynamics and allow the improvement of services for the citizens. In these urban centers, different methodologies are used in order to collect data and provide them to applications. These data come from several heterogeneous sources, thus there is an effort to integrate and standardize them before their use. Also, a significant amount of this data has spatio-temporal annotations, which may be used to analyze the city dynamics, such as the mobility flow. Due to these characteristics of the data generated in urban centers, and also the possibilities brought by their use and analyses, this work presents a novel approach to collect, integrate and perform some analysis tasks in mobility data from smart cities. Thus, the SMAFramework can analyze mobility patterns based on a Multi-Aspect Graph (MAG) data structure. To show the potential of the framework, it is proposed a method to analyze the saptio-temporal correlation between data from two different data sources in the same city. Real data collected from social media and a taxi system of the city of New York are used to evaluate this method. The obtained results allowed to understand some of the applicabilities of the framework and also provided some insights on how to use the framework to resolve specific problems when analyzing mobility in urban environments.

Graph data structures constitute fundamental components within computer science, serving as versatile models for representing interconnected relationships in a myriad of domains, including social networks, transportation systems, and biological networks.However, comprehending the intricacies inherent to graphs often poses a significant challenge for students enrolled in data structures courses.This paper endeavors to explore the transformative potential of visualization techniques in bolstering comprehension and engagement within the realm of graph data structures education.Drawing upon insights gleaned from cognitive psychology and educational research, we embark on an examination of how visualization methodologies can serve as catalysts for fostering deeper understanding and retention of abstract concepts, thereby elevating overall learning outcomes.Through literature review, we meticulously scrutinize a number of studies, each illuminating the efficacy of graph visualization tools in enhancing student comprehension and problem-solving skills.Moreover, we ardently advocate for the adoption of an active learning paradigm, wherein students are not merely passive recipients of knowledge but are actively encouraged to develop their visualization applications.Such an approach not only nurtures creativity and innovation, but also engenders a profound appreciation for the intricate nuances inherent to graph representations.Additionally, we delve into the underlying principles and methodologies of graph visualization, offering a discourse on techniques for crafting visually compelling graph layouts.By synthesizing a rich tapestry of research findings and practical insights, this paper endeavors to furnish educators with some strategies for seamlessly integrating visualization techniques into the fabric of data structures curricula.Ultimately, our overarching objective is to empower students to navigate the labyrinthine complexities of graph data structures with unwavering confidence, consummate proficiency, and reverence for the elegance imbued within graphical representations.

Evolving graph processing has enabled the modeling of many complex network systems, e.g., online social networks and gene networks. Existing in-memory graph data structures cannot effectively exploit the current and ongoing adoption of emerging non-volatile main memory (NVMM) for two reasons. (1) Ephemeral graph data structures are not crash-consistent nor durable for NVMM. Corruption is likely when updating its correlated application-defined data and runtime states in the face of hardware or software failures. (2) NVMM writes and reads may incur higher latency than DRAM. Placing the data structures in NVMM may result in a significant loss of performance. In this paper, we propose a novel persistent evolving graph data structure, named NVGRAPH, for both computing and in-memory storage of evolving graphs in NVMM. We devise NVGRAPH as a multi-version data structure, wherein a minimum of one version of its data is stored in NVMM to provide the desired durability at runtime for failure recovery, and another version is stored in both DRAM and NVMM to reduce the NVMM-induced memory latency. We dynamically transform the layout of NVGRAPH including changing the size of its partition in DRAM and the position of its base snapshot exploiting network properties and data access patterns of workloads. For the evaluation of NVGRAPH, we implement four representative real-world graph applications: pagerank, BFS, influence maximization, and rumor source detection. The experimental results show that the performance of NVGRAPH is comparable to other in-memory data structure (e.g., CSR and LLAMA) while using 70% less DRAM. It scales well up to 10 billion edges and 201 snapshots and supports crash consistency. It offers up to the 21X speedup of execution time compared to the scale-up graph computation approaches (e.g., GraphChi and X-stream).

The objective is to develop a data structure that is capable of handling large data volumes and offers support for querying, analysis and validation. Based on earlier results (i.e. the full decomposition of space, the use of a TEN structure and applying Poincare simplicial homology as mathematical foundation) a simplicial complex-based TEN structure is developed. Applying simplicial homology offers full control over orientation of simplexes and enables one to derive substantial parts of the TEN structure, instead of explicitly store the entire network. The described data structure is developed as a DBMS data structure and the usage of views, function based indexes and 3D R-trees result in a compact topological 3D data structure. Theoretical aspects of this approach are described earlier [12, 17,18]. This paper describes both theory and implementation of the approach.

This paper presents and investigates methods for fast and accurate illumination of scenes containing many light sources that have limited spatial influence, e.g. point light sources. For speeding up the computation, current graphics applications use an assumption that the light sources range can be limited using bounding spheres due to their limited spatial influence and illumination is computed only if a surface lies within the sphere. Therefore, we explore the differences in illumination between scenes illuminated with spatially limited light sources and physically more correct computation where the light radius is infinite. We show that the difference can be small if we add appropriate ambient lighting. The contribution of the paper is the method for fast estimation of ambient lighting in scenes illuminated by numerous light sources. We also propose a method for elimination of color discontinuities at the edges of the bounding spheres. Our solution is tested on two different scenes: a procedurally generated city and the Sibenik cathedral. Our approach allows for correct lighting computation in scenes with numerous light sources without any modification of the scene graph or other data structures or rendering procedures. It thus can be applied in various systems without any structural modifications.

Tourism is growing very rapidly with the development of technology, especially information technology .Through information technology people can easily get access to information about various destinations and tourist attractions as well as hotel by online media communication. Therefore, this study was trying to use social media such as facebook and twitter, where both social media were widely used by companies engaged in tourism in the world as well as in Bali. Internet consists of several websites, but the most effective one to conduct the research on tourism such as webseries 2.0 or commonly called as social networking media. Social medias - Facebook and Twitter - both were wildly used by the companies running in the sector of tourism in Bali, such as Hotels, Villas, Restaurants, Spas, Travel Agents, Airlines, and Tourist Attraction Managements, to advertised their products and services in their respective field. Through those social media, tourists will be able to access the comments or reviews about the companies. The problem raised in this research is how Bali’s tourism data in social networking media were used and considered, and how was the image of Bali’s tourism in their point of viewbased on the research. The method used here was descriptive qualitative method which describes this phenomenon descriptively by analyzing the obtained comments on social networking media such as facebook and twitter, and then classified in the form of comments positive, negative, and unidentified comment. Afterwards comments were re-analyzed through 4A Approach which consist of Attraction, amenities, Ancillary, and Accessibility. Data which has been obtained re-analyzed via the data obtained from the homepage of (TripAdvisor and Agoda.com). In addition, to strengthen the research data obtained from the homepage of TripAdvisor and Agoda.com , then it has been elaborated on the point rating system which indicatedthat the company were registered in both of those social networking media such as Hotel, Villa, SPA, restaurants, travel agents, airlines, media, tourist attraction management has a good rating result, and it has represented that the image of Bali tourism has a good rank in the eyes of local and international tourists. This data was also strengthened by the existence of empirical studies where there were the data from a hundred tourists came to Bali both locally and internationally has filled out the questionnaire about Bali in order to obtain maximum results on the image of Bali tourism. The results showed that, social networking media were effectively used in this study. Many companies in Bali used facebook and twitter. In addition, Bali Tourism image placed in positive rank on both of social networking sites, and it was gained by the results of questionnaires spread towards the visitor.

To leverage modern hardware platforms to their fullest, more and more database systems embrace compilation of query plans to native code. In the research community, there is an ongoing debate about the best way to architect such query compilers. This is perceived to be a difficult task, requiring techniques fundamentally different from traditional interpreted query execution. In this dissertation, we contribute to this discussion by drawing attention to an old but underappreciated idea known as Futamura projections, which fundamentally link interpreters and compilers. Guided by this idea, we demonstrate that efficient query compilation can actually be very simple, using techniques that are no more difficult than writing a query interpreter in a high-level language. We first develop LB2: a high-level query compiler implemented in this style that is competitive with the best compiled query engines both in sequential and parallel execution on the standard TPC-H benchmark. Query engines process a variety of data types and structures including text, spatial, graphs, etc. Several spatial and graph engines are implemented as extensions to relational query engines to leverage optimized memory, storage, and evaluation. Still, the performance of these extensions is often stymied by the interpretive nature of the underlying data management, generic data structures, and the need to execute domain-specific external libraries. On that basis, compiling spatial and graph queries to native code is a desirable avenue to mitigate existing limitations and improve performance. To support compiling spatial queries, we extend the LB2 main-memory query compiler with spatial predicates, indexing structures, and spatial operators. To support compiling graph queries, we extend LB2 with graph data structures and operators. The spatial extension matches the performance of hand-written code and outperforms relational query engines and map-reduce extensions. Similarly, the graph extension matches, and sometimes outperforms, low-level graph engines.

The paper describes graph algorithms performance when using different types of data structures. To achieve that, we developed a multi-level graph processing system, which allows to create graph applications independently of any implementation details such as graph data structure or underlying computational architecture. We measure the performance of breadth-first search, max flow and random graph building algorithms when using compressed sparse row and adjacency matrix data structures. Experiments reveal different graph processing rates for different data structures, which indicates the need of using specific data structures for specific algorithms to achieve highest performance.