Change search
Link to record
Permanent link

Direct link
BETA
Girdzijauskas, SarunasORCID iD iconorcid.org/0000-0003-4516-7317
Publications (9 of 9) Show all publications
Holst, A., Bouguelia, M.-R. -., Görnerup, O., Pashami, S., Al-Shishtawy, A., Falkman, G., . . . Soliman, A. (2019). Eliciting structure in data. In: CEUR Workshop Proceedings: . Paper presented at 2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, 20 March 2019.
Open this publication in new window or tab >>Eliciting structure in data
Show others...
2019 (English)In: CEUR Workshop Proceedings, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper demonstrates how to explore and visualize different types of structure in data, including clusters, anomalies, causal relations, and higher order relations. The methods are developed with the goal of being as automatic as possible and applicable to massive, streaming, and distributed data. Finally, a decentralized learning scheme is discussed, enabling finding structure in the data without collecting the data centrally. © 2019 Copyright held for the individual papers by the papers’ authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

Keywords
Anomaly detection, Causal inference, Clustering, Distributed analytics, Higher-order structure, Information visualization, Information systems, User interfaces, Causal inferences, Data acquisition
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-38261 (URN)2-s2.0-85063227224 (Scopus ID)
Conference
2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, 20 March 2019
Available from: 2019-04-02 Created: 2019-04-02 Last updated: 2019-04-02Bibliographically approved
Abbas, Z., Al-Shishtawy, A., Girdzijauskas, S. & Vlassov, V. (2018). Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks. In: Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services: . Paper presented at 7th IEEE International Congress on Big Data, BigData Congress 2018, 2 July 2018 through 7 July 2018 (pp. 57-65). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks
2018 (English)In: Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 57-65Conference paper, Published paper (Refereed)
Abstract [en]

Short-term traffic prediction allows Intelligent Transport Systems to proactively respond to events before they happen. With the rapid increase in the amount, quality, and detail of traffic data, new techniques are required that can exploit the information in the data in order to provide better results while being able to scale and cope with increasing amounts of data and growing cities. We propose and compare three models for short-term road traffic density prediction based on Long Short-Term Memory (LSTM) neural networks. We have trained the models using real traffic data collected by Motorway Control System in Stockholm that monitors highways and collects flow and speed data per lane every minute from radar sensors. In order to deal with the challenge of scale and to improve prediction accuracy, we propose to partition the road network into road stretches and junctions, and to model each of the partitions with one or more LSTM neural networks. Our evaluation results show that partitioning of roads improves the prediction accuracy by reducing the root mean square error by the factor of 5. We show that we can reduce the complexity of LSTM network by limiting the number of input sensors, on average to 35% of the original number, without compromising the prediction accuracy. .

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Keywords
LSTM, neural networks, traffic prediction, Big data, Brain, Forecasting, Intelligent systems, Mean square error, Roads and streets, Traffic control, Evaluation results, Input sensor, Intelligent transport systems, Prediction accuracy, Radar sensors, Root mean square errors, Long short-term memory
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:ri:diva-37285 (URN)10.1109/BigDataCongress.2018.00015 (DOI)2-s2.0-85054887478 (Scopus ID)9781538672327 (ISBN)
Conference
7th IEEE International Congress on Big Data, BigData Congress 2018, 2 July 2018 through 7 July 2018
Note

Funding details: Fellowships Fund Incorporated, FFI; Funding details: VINNOVA; Funding details: 20140221; Funding details: European Commission, EC, FPA 2012-0030; Funding details: Education, Audiovisual and Culture Executive Agency, EACEA; Funding details: Directorate-General for Research and Innovation, 2015-00677; Funding text 1: ACKNOWLEDGMENT This work was supported by the project BADA: Big Automotive Data Analytics in the funding program FFI: Strategic Vehicle Research and Innovation (grant 2015-00677) administrated by VINNOVA the Swedish government agency for innovation systems, by the project BIDAF: Big Data Analytics Framework for a Smart Society (grant 20140221) funded by KKS the Swedish Knowledge Foundation, and by the Erasmus Mundus Joint Doctorate in Distributed Computing (EMJD-DC) programme funded by the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission under FPA 2012-0030.

Available from: 2019-01-18 Created: 2019-01-18 Last updated: 2019-03-29Bibliographically approved
Ghoorchian, K., Girdzijauskas, S. & Rahimian, F. (2017). DeGPar: Large Scale Topic Detection Using Node-Cut Partitioning on Dense Weighted Graphs. In: Proceedings - International Conference on Distributed Computing Systems: . Paper presented at 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017, 5 June 2017 through 8 June 2017 (pp. 775-785).
Open this publication in new window or tab >>DeGPar: Large Scale Topic Detection Using Node-Cut Partitioning on Dense Weighted Graphs
2017 (English)In: Proceedings - International Conference on Distributed Computing Systems, 2017, p. 775-785Conference paper, Published paper (Refereed)
Abstract [en]

Topic Detection (TD) refers to automatic techniques for locating topically related material in web documents. Nowadays, massive amounts of documents are generated by users of Online Social Networks (OSNs), in form of very short text, tweets and snippets of news. While topic detection, in its traditional form, is applied to a few documents containing a lot of information, the problem has now changed to dealing with massive number of documents with very little information. The traditional solutions, thus, fall short either in scalability (due to huge number of input items) or sparsity (due to insufficient information per input item). In this paper we address the scalability problem by introducing an efficient and scalable graph based algorithm for TD on short texts, leveraging dimensionality reduction and clustering techniques. We first, compress the input set of documents into a dense graph, such that frequent cooccurrence patterns in the documents create multiple dense topological areas in the graph. Then, we partition the graph into multiple dense sub-graphs, each representing a topic. We compare the accuracy and scalability of our solution with two state-of-the-art solutions (including the standard LDA, and BiTerm). The results on two widely used benchmark datasets show that our algorithm not only maintains a similar or better accuracy, but also performs by an order of magnitude faster than the state-of-the-art approaches.

Keywords
Dense Weighted Graph Partitioning, Dimensionality Reduction, Distributed Algorithms, Node-cut Graph Partitioning, Online Social Networks, Random Indexing, Topic Detection, Clustering algorithms, Distributed computer systems, Graphic methods, Parallel algorithms, Scalability, Scales (weighing instruments), Social networking (online), Topology, Graph Partitioning, On-line social networks, Weighted graph, Graph theory
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-30836 (URN)10.1109/ICDCS.2017.19 (DOI)2-s2.0-85027258993 (Scopus ID)9781538617915 (ISBN)
Conference
37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017, 5 June 2017 through 8 June 2017
Available from: 2017-09-07 Created: 2017-09-07 Last updated: 2018-07-20Bibliographically approved
Rahimian, F., Girdzijauskas, S. & Haridi, S. (2014). Parallel Community Detection For Cross-Document Coreference (6ed.). Kista, Sweden: Swedish Institute of Computer Science
Open this publication in new window or tab >>Parallel Community Detection For Cross-Document Coreference
2014 (English)Report (Other academic)
Abstract [en]

This document presents a highly parallel solution for cross-document coreference resolution, which can deal with billions of documents that exist in the current web. At the core of our solution lies a novel algorithm for community detection in large scale graphs. We operate on graphs which we construct by representing documents' keywords as nodes and the co-location of those keywords in a document as edges. We then exploit the particular nature of such graphs where coreferent words are topologically clustered and can be efficiently discovered by our community detection algorithm. The accuracy of our technique is considerably higher than that of the state of the art, while the convergence time is by far shorter. In particular, we increase the accuracy for a baseline dataset by more than 15\% compared to the best reported result so far. Moreover, we outperform the best reported result for a dataset provided for the Word Sense Induction task in SemEval 2010.

Place, publisher, year, edition, pages
Kista, Sweden: Swedish Institute of Computer Science, 2014 Edition: 6
Series
SICS Technical Report, ISSN 1100-3154 ; 2014:01
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24302 (URN)
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-20Bibliographically approved
Rahimian, F., Payberah, A., Girdzijauskas, S., Jelasity, M. & Haridi, S. (2013). Ja-be-Ja: A Distributed Algorithm for Balanced Graph Partitioning (7ed.). Kista, Sweden: Swedish Institute of Computer Science
Open this publication in new window or tab >>Ja-be-Ja: A Distributed Algorithm for Balanced Graph Partitioning
Show others...
2013 (English)Report (Other academic)
Abstract [en]

Balanced graph partitioning is a well known NP-complete problem with a wide range of applications. These applications include many large-scale distributed problems such as the optimal storage of large sets of graph-structured data over several hosts, or identifying clusters in on-line social networks. In such very large-scale distributed scenarios, state-of-the-art algorithms are not directly applicable, because they typically involve frequent global operations over the entire graph. In this paper, we propose a distributed graph partitioning algorithm, called Ja-be-Ja1. The algorithm is massively parallel: each graph node is processed independently, and only the direct neighbors of the node, and a small subset of random nodes in the graph need to be known. Strict synchronization is not required. These features allow Ja-be-Ja to be easily adapted to any distributed graph-processing system from data centers to fully distributed networks. We perform a thorough experimental analysis, which shows that the minimal edge-cut value achieved by Ja-be-Ja is comparable to state-of-the-art centralized algorithms such as Metis. In particular, on large social networks Ja-be-Ja outperforms Metis.

Place, publisher, year, edition, pages
Kista, Sweden: Swedish Institute of Computer Science, 2013 Edition: 7
Series
SICS Technical Report, ISSN 1100-3154 ; 2013:03
Keywords
graph partitioning, distributed algorithm, load balancing
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24165 (URN)
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2019-06-28Bibliographically approved
Rahimian, F., Girdzijauskas, S., Payberah, A. & Haridi, S. (2011). Vinifera: A Gossip-based Hybrid Overlay for Content-based Publish/Subscribe (10ed.). In: : . Paper presented at CNS Workshop, November 2011.
Open this publication in new window or tab >>Vinifera: A Gossip-based Hybrid Overlay for Content-based Publish/Subscribe
2011 (English)Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-23949 (URN)
Conference
CNS Workshop, November 2011
Projects
CNS
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2019-06-28Bibliographically approved
Rahimian, F., Girdzijauskas, S., Payberah, A. & Haridi, S. (2011). Vitis: A Gossip-based Hybrid Overlay for Internet-scale Publish/Subscribe (11ed.). In: IPDPS 2011: . Paper presented at IPDPS 2011.
Open this publication in new window or tab >>Vitis: A Gossip-based Hybrid Overlay for Internet-scale Publish/Subscribe
2011 (English)In: IPDPS 2011, 2011, 11Conference paper, Published paper (Refereed)
Abstract [en]

Peer-to-peer overlay networks are attractive solutions for building Internet-scale publish/subscribe systems. However, scalability comes with a cost: a message published on a certain topic often needs to traverse a large number of uninterested (unsubscribed) nodes before reaching all its subscribers. This might sharply increase resource consumption for such relay nodes (in terms of bandwidth transmission cost, CPU, etc) and could ultimately lead to rapid deterioration of the system’s performance once the relay nodes start dropping the messages or choose to permanently abandon the system. In this paper, we introduce Vitis, a gossip-based publish/subscribe system that significantly decreases the number of relay messages, and scales to an unbounded number of nodes and topics. This is achieved by the novel approach of enabling rendezvous routing on unstructured overlays. We construct a hybrid system by injecting structure into an otherwise unstructured network. The resulting structure resembles a navigable small-world network, which spans along clusters of nodes that have similar subscriptions. The properties of such an overlay make it an ideal platform for efficient data dissemination in large-scale systems. We perform extensive simulations and evaluate Vitis by comparing its performance against two base-line publish/subscribe systems: one that is oblivious to node subscriptions, and another that exploits the subscription similarities. Our measurements show that Vitis significantly outperforms the base-line solutions on various subscription and churn scenarios, from both synthetic models and real-world traces.

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-23843 (URN)
Conference
IPDPS 2011
Projects
Vitis
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2019-06-28Bibliographically approved
Girdzijauskas, S., Chockler, G., Vigfusson, Y., Tock, Y. & Melamed, R. (2010). Magnet: Practical Subscription Clustering for Internet-Scale Publish/Subscribe (11ed.). In: : . Paper presented at The 4th ACM International Conference on Distributed Event-Based Systems (DEBS).
Open this publication in new window or tab >>Magnet: Practical Subscription Clustering for Internet-Scale Publish/Subscribe
Show others...
2010 (English)Conference paper, Published paper (Refereed)
Abstract [en]

An effective means for building Internet-scale distributed applications, and in particular those involving group-based information sharing, is to deploy peer-to-peer overlay networks. The key pre-requisite for supporting these types of applications on top of the overlays is efficient distribution of messages to multiple subscribers dispersed across numerous multicast groups. In this paper, we introduce Magnet: a peer-to-peer publish/ subscribe system which achieves efficient message distribution by dynamically organizing peers with similar subscriptions into dissemination structures which preserve locality in the subscription space. Magnet is able to significantly reduce the message propagation costs by taking advantage of subscription correlations present in many large-scale groupbased applications. We evaluate Magnet by comparing its performance against a strawman pub/sub system which does not cluster similar subscriptions by simulation. We find that Magnet outperforms the strawman by a substantial margin on clustered subscription workloads produced using both generative models and real application traces.

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-23789 (URN)
Conference
The 4th ACM International Conference on Distributed Event-Based Systems (DEBS)
Projects
NetInf
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-07-20Bibliographically approved
Aberer, K., Onana Alima, L., Ghodsi, A., Girdzijauskas, S., Hauswirth, M. & Haridi, S. (2005). The essence of P2P: A reference architecture for overlay networks (1ed.). In: : . Paper presented at 5th IEEE International Conference on Peer-to-Peer Computing.
Open this publication in new window or tab >>The essence of P2P: A reference architecture for overlay networks
Show others...
2005 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The success of the P2P idea has created a huge diversity of approaches, among which overlay networks, for example, Gnutella, Kazaa, Chord, Pastry, Tapestry, P-Grid, or DKS, have received specific attention from both developers and researchers. A wide variety of algorithms, data structures, and architectures have been proposed. The terminologies and abstractions used, however, have become quite inconsistent since the P2P paradigm has attracted people from many different communities, e.g., networking, databases, distributed systems, graph theory, complexity theory, biology, etc. In this paper we propose a reference model for overlay networks which is capable of modeling different approaches in this domain in a generic manner. It is intended to allow researchers and users to assess the properties of concrete systems, to establish a common vocabulary for scientific discussion, to facilitate the qualitative comparison of the systems, and to serve as the basis for defining a standardized API to make overlay networks interoperable.

Publisher
p. 10
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-21108 (URN)
Conference
5th IEEE International Conference on Peer-to-Peer Computing
Projects
EVERGROW
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-20Bibliographically approved
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4516-7317

Search in DiVA

Show all publications
v. 2.35.7