A demonstration of the G&amp;#x2217; graph database system

Spillane, Sean R.; Birnbaum, Jeremy; Bokser, D.; Kemp, David B.; Labouseur, Alan G.; Olsen, Paul W.; Vijayan, Jayadevan; Hwang, Jeong-Hyon; Yoon, JunWeon

doi:10.1109/icde.2013.6544943

Cited by 9 publications

(8 citation statements)

References 4 publications

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“…This technique does not only save space but also speeds up retrieval. The graph management system G * [41] also avoids saving redundancies, albeit without an explicit reference graph, but capitalizes on node identity in distinct graphs. Again, none of these compression techniques can be directly applied for compressed storage of 4D trajectories.…”

Section: Storage Of Four-dimensionalmentioning

confidence: 99%

Efficient Compression of 4D-Trajectory Data in Air Traffic Management

Wandelt

Sun

2014

IEEE Trans. Intell. Transport. Syst.

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Air traffic management (ATM) is facing a tremendous increase in the amount of available flight data, particularly four-dimensional (4D) trajectories. Computational requirements for analysis and storage of such bulk of data are steeply increasing. Compression is one key technology to address this challenge. In this paper we propose two techniques for compressing air traffic 4D trajectories. Our first technique analyzes a set of samples and computes a prediction for the most likely picked successor coordinate by a random walker. The second technique, i.e., referential compression, compresses a 4D trajectory as a collection of subtrajectory pointers into a reference trajectory. We evaluate our algorithms on trajectory data from the Demand Data Repository provided by EUROCONTROL. We show that a combination of our referential and statistical compression techniques compresses 4D trajectories of all air traffic over Europe in the year 2013 from 60 GB down to 0.78 GB, achieving a compression ratio of more than 75 : 1. The compression ratio for our techniques increases with the number of to-be-compressed flights, whereas standard compression techniques achieve a fixed compressed ratio for any number of flights. Our work contributes toward efficient handling of the increasing amount of traffic data in ATM.Index Terms-Air traffic management (ATM), compression, four dimensional (4D) trajectories.

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Section: Storage Of Four-dimensionalmentioning

confidence: 99%

Efficient Compression of 4D-Trajectory Data in Air Traffic Management

Wandelt

Sun

2014

IEEE Trans. Intell. Transport. Syst.

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“…Graph query tools. In [12], Spillane et al developed a system G*, which stores graphs on a large number of servers and allows users to easily express graph queries. In [2], Fan et al presented ExpFinder, a system for finding experts in social networks based on graph pattern matching.…”

Section: Related Work and Noveltymentioning

confidence: 99%

C-explorer

et al. 2017

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Community retrieval (CR) algorithms, which enable the extraction of subgraphs from large social networks (e.g., Facebook and Twitter), have attracted tremendous interest. Various CR solutions, such as k-core and CODICIL, have been proposed to obtain graphs whose vertices are closely related. In this paper, we propose the C-Explorer system to assist users in extracting, visualizing, and analyzing communities. C-Explorer provides online and interactive CR facilities, allowing a user to view her interesting graphs, indicate her required vertex q, and display the communities to which q belongs. A seminal feature of C-Explorer is that it uses an attributed graph, whose vertices are associated with labels and keywords, and looks for an attributed community (or AC), whose vertices are structurally and semantically related. Moreover, C-Explorer implements several state-of-the-art CR algorithms, as well as functions for analyzing their effectiveness. We plan to make C-Explorer an open-source web-based platform, and design API functions for software developers to test their CR algorithms in our system.

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“…In this section the attention turns to systems that were proposed for parallel and distributed environments. The systems that will be analyzed are the DeltaGraph system by Khurana et al [65], the G* graph database by Labouseur et al [76,107,77] and the GreyCat system by Hartmann et al [51].…”

Section: Parallel and Distributed Methodsmentioning

confidence: 99%

“…In the parallel and distributed setting there have been three main methods proposed: The DeltaGraph system [65] (along with its extension HGS [67]) is based on the principle of deltas and aims to efficiently store and retrieve the graph at specific time instances. The G* system [76,107,77] is a parallel graph database that focuses on taking advantage of the commonalities present between a graph in different time instances to store the sequence in an efficient manner. Finally, the GreyCat system [51] adopts a multiple-world approach to the evolution of graph sequences that permits large scale what-if analysis.…”

Section: Historical Information Managementmentioning

confidence: 99%

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Management of historical information and preference in massive datasets

Kosmatopoulos¹

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H εκρηκτική αύξηση του ρυθμού παραγωγής δεδομένων η οποία παρατηρείται από πλήθος διαφορετικών πηγών, αποτελεί ένα χαρακτηριστικό φαινόμενο των τελευταίων δύο δεκαετιών. Ιστοσελίδες παροχής περιεχομένου, κοινωνικά δίκτυα καθώς επίσης και επιχειρήσεις ή οργανισμοί διαφόρων πεδίων όπως Αστρονομία και Γονιδιωματική έχουν καθημερινές ανάγκες αποθήκευσης και επεξεργασίας δεδομένων οι οποίες απαιτούν ένα σημαντικό πλήθος πόρων. Προκύπτει, επομένως, η ανάγκη εύρεσης λύσεων στα προβλήματα των σύγχρονων εφαρμογών οι οποίες θα χαρακτηρίζονται τόσο από χρονική όσο και από χωρική αποδοτικότητα. Για το σκοπό αυτό, η παρούσα διατριβή μελετάει ζητήματα που σχετίζονται με τον αποδοτικό υπολογισμό ερωτημάτων προτίμησης καθώς και την αποδοτική διαχείριση ιστορικής πληροφορίας προτείνοντας αλγορίθμους και δομές δεδομένων με γραμμικό χωρικό κόστος. Πιο συγκεκριμένα, το πρώτο μέρος της διατριβής ασχολείται με τη δυναμική διατήρηση των k κορυφαίων σημείων κυριαρχίας και τον υπολογισμό 3-πλευρών ερωτημάτων κορυφογραμμής σε πλήρως δυναμικά σύνολα δεδομένων. Το δεύτερο μέρος της διατριβής παρουσιάζει ένα πρότυπο σύστημα με το όνομα HiNode το οποίο διαχειρίζεται ιστορική πληροφορία σε ακολουθίες εξελισσόμενων γραφημάτων και χαρακτηρίζεται από ασυμπτωτικά βέλτιστο χωρικό κόστος. Τέλος, περιγράφονται δύο υλοποιήσεις για το σύστημα HiNode, που βασίζονται σε διαφορετικές υποκείμενες τεχνολογίες, οι οποίες ακολούθως αξιολογούνται πειραματικά.

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A demonstration of the G∗ graph database system

Cited by 9 publications

References 4 publications

Efficient Compression of 4D-Trajectory Data in Air Traffic Management

Efficient Compression of 4D-Trajectory Data in Air Traffic Management

C-explorer

Management of historical information and preference in massive datasets

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