On querying historical evolving graph sequences

Ren, Chenghui; Lo, Eric; Kao, Ben; Zhu, Xinjie; Cheng, Reynold

doi:10.14778/3402707.3402713

Cited by 78 publications

(51 citation statements)

References 22 publications

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“…Existent works examine valuable insights into the dynamic world by posing queries on an evolving sequence of social graphs (e.g. [146]). Time evolving graphs are increasingly used as a paradigm for the emerging area of OSNs [71].…”

Section: Content Distributionmentioning

confidence: 99%

“…In the direction of studying the information diffusion as social graphs evolve, Ren et al [146] study the evolution steps for shortest paths between two nodes, (so that they can ascribe them to a disjoint path, a short-circuiting bridge or a new friend between them), and furthermore, metrics such as closeness centrality, and global metrics, like the graph diameter, across snapshots of gradually evolving graphs. To this end, they adopt an efficient algorithm and an efficient storage scheme.…”

Section: Independent Cascade Modelmentioning

confidence: 99%

See 1 more Smart Citation

Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

Kilanioti

Fernández-Montes

Fernández-Cerero

et al. 2019

Lecture Notes in Computer Science

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This chapter presents the authors' work for the Case Study entitled "Delivering Social Media with Scalability" within the framework of High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) COST Action 1406. We identify some core research areas and give an outline of the publications we came up within the framework of the aforementioned action. The ease of user content generation within social media platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges: derivation of real-time meaningful insights from effectively gathered social information, a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general, etc. In this article we present the methodology we followed, the results of our work and the outline of a comprehensive survey, that depicts the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centers supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. The challenges of enabling better provisioning of social media data have been identified and they were based on the context of users accessing these resources. The existing literature has been systematized and the main research points and industrial efforts in the area were identified and analyzed. In our works, in the framework of the Action, we came up with

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Section: Content Distributionmentioning

confidence: 99%

Section: Independent Cascade Modelmentioning

confidence: 99%

Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

Kilanioti

Fernández-Montes

Fernández-Cerero

et al. 2019

Lecture Notes in Computer Science

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“…[36] proposes the GIM-V incremental graph processing model based upon matrix-vector operations. [25] constructs representative snapshots which are initially used for querying and upon success uses real snapshots. Naiad [23] incorporates differential data flow to perform iterative and incremental algorithms.…”

Section: Related Workmentioning

confidence: 99%

KickStarter

Vora

Gupta

2017

SIGOPS Oper. Syst. Rev.

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Continuous processing of a streaming graph maintains an approximate result of the iterative computation on a recent version of the graph. Upon a user query, the accurate result on the current graph can be quickly computed by feeding the approximate results to the iterative computation -a form of incremental computation that corrects the (small amount of) error in the approximate result. Despite the effectiveness of this approach in processing growing graphs, it is generally not applicable when edge deletions are present -existing approximations can lead to either incorrect results (e.g., monotonic computations terminate at an incorrect minima/maxima) or poor performance (e.g., with approximations, convergence takes longer than performing the computation from scratch). This paper presents KickStarter, a runtime technique that can trim the approximate values for a subset of vertices impacted by the deleted edges. The trimmed approximation is both safe and profitable, enabling the computation to produce correct results and converge quickly. KickStarter works for a class of monotonic graph algorithms and can be readily incorporated in any existing streaming graph system. Our experiments with four streaming algorithms on five large graphs demonstrate that trimming not only produces correct results but also accelerates these algorithms by 8.5-23.7×.

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“…The existing provenance models, e.g., the open provenance model (OPM) [22], treat time as a second class citizen (i.e., as an optional annotation of the data) which will result in loosing semantics of time and makes querying and analyzing provenance data for a particular point in time inefficient and sometimes inaccessible. For example, the shortest path from a business artifact to its origin may change over time [26] as provenance metadata forms a large, dynamic, and time-evolving graph. In particular, versioning and provenance are important cross-cutting aspects of business artifacts and should be considered in modeling the evolution of artifacts over time.…”

Section: Representing Cross-cutting Aspectsmentioning

confidence: 99%

“…Modeling/Querying Temporal Graphs. In recent years, a plethora of work [16,19,26] has focused on temporal graphs to model evolving, time-varying, and dynamic networks of data. Ren et al [26] proposed a historical graphstructure to maintain analytical processing on such evolving graphs.…”

Section: Related Workmentioning

confidence: 99%

Enabling the Analysis of Cross-Cutting Aspects in Ad-Hoc Processes

Beheshti

Benatallah

Motahari-Nezhad

2013

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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Processes in case management applications are flexible, knowledge-intensive and people-driven, and often used as guides for workers in processing of artifacts. An important fact is the evolution of process artifacts over time as they are touched by different people in the context of a knowledge-intensive process. This highlights the need for tracking process artifacts in order to find out their history (artifact versioning) and also provenance (where they come from, and who touched and did what on them). We present a framework, simple abstractions and a language for analyzing cross-cutting aspects (in particular versioning and provenance) over process artifacts. We introduce two concepts of timedfolders to represent evolution of artifacts over time, and activity-paths to represent the process which led to artifacts. The introduced approaches have been implemented on top of FPSPARQL, Folder-Path enabled extension of SPARQL, and experimentally validated on real-world datasets.

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On querying historical evolving graph sequences

Cited by 78 publications

References 22 publications

Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

KickStarter

Enabling the Analysis of Cross-Cutting Aspects in Ad-Hoc Processes

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