Techniques for Graph Analytics on Big Data

Nisar, Muhammad Usman; Fard, Arash; Miller, John A.

doi:10.1109/bigdata.congress.2013.78

Cited by 30 publications

(8 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Graphs are a technique for modeling relations in any field [15,16]. In social graphs, they are used to measure a persons' prestige and his ability to influence others.…”

Section: Data Randd or Data Discovery Platformmentioning

confidence: 99%

Big Data Architecture

Bhashyam

2015

Studies in Big Data

View full text Add to dashboard Cite

Big data is a broad descriptive term for non-transactional data that are user generated and machine generated. Data generation evolved from transactional data to first interaction data and then sensor data. Web log was the first step in this evolution. These machines generated logs of internet activity caused the first growth of data. Social media pushed data production higher with human interactions.

show abstract

“…Graphs are a technique for modeling relations in any field [15,16]. In social graphs, they are used to measure a persons' prestige and his ability to influence others.…”

Section: Data Randd or Data Discovery Platformmentioning

confidence: 99%

Big Data Architecture

Bhashyam

2015

Studies in Big Data

View full text Add to dashboard Cite

show abstract

“…It connects each vertex to a random number of other vertices based on a given desired average degree. Similar to [12], [13], we define a variable α to describe the relationship between the number of edges and the number of vertices in the graph, such that |E| = |V | α . We use α = 1.2 as used by others [12], [13], unless specified.…”

Section: Experimentationmentioning

confidence: 99%

“…Similar to [12], [13], we define a variable α to describe the relationship between the number of edges and the number of vertices in the graph, such that |E| = |V | α . We use α = 1.2 as used by others [12], [13], unless specified. The second technique generates graphs with power law degree distributions, as can be found in many real world graphs [2].…”

Section: Experimentationmentioning

confidence: 99%

See 1 more Smart Citation

DualIso: An Algorithm for Subgraph Pattern Matching on Very Large Labeled Graphs

Saltz

Jain

Kothari

et al. 2014

2014 IEEE International Congress on Big Data

Self Cite

View full text Add to dashboard Cite

An important component of current research in big data is graph analytics on very large graphs. Of the many problems of interest in this domain, graph pattern matching is both challenging and practically important. The problem is, given a relatively small query graph, finding matching patterns in a large data graph. Algorithms to address this problem are used in large social networks and graph databases. Though fast querying is highly desirable, the scalability of pattern matching algorithms is hindered by the NP-completeness of the subgraph isomorphism problem. This paper presents a conceptually simple, memory-efficient, pruning-based algorithm for the subgraph isomorphism problem that outperforms commonly used algorithms on large graphs. The high performance is due in large part to the effectiveness of the pruning algorithm, which in many cases removes a large percentage of the vertices not found in isomorphic matches. In this paper, the runtime of the algorithm is tested alongside other algorithms on graphs of up to 10 million vertices and 250 million edges.

show abstract

“…To this end, the information contained in the semantically annotated text documents (produced by semantic engines) are modelled as graphs that contain entities, their relationships and other relevant elements providing information on the context. This design choice has a twofold advantage: it enables the creation of inter-document relationships by connecting graphs related to different documents; it allows us to leverage the stateof-the-art on graph analytics, management and visualization algorithms (Nisar, Fard, & Miller, 2013) (Nguyen, Lenharth, & Pingali, 2013). •…”

mentioning

confidence: 99%

Supporting sense-making and decision-making through time evolution analysis of open sources

Balboni

Marchetti

Colajanni

et al. 2015

2015 7th International Conference on Cyber Conflict: Architectures in Cyberspace

View full text Add to dashboard Cite

Modern societies produce a huge amount of open source information that is often published on the Web in a natural language form. The impossibility of reading all these documents is paving the way to semantic-based technologies that are able to extract from unstructured documents relevant information for analysts. Most solutions extract uncorrelated pieces of information from individual documents; few of them create links among related documents and, to the best of our knowledge, no technology focuses on the time evolution of relations among entities. We propose a novel approach for managing, querying and visualizing temporal knowledge extracted from unstructured documents that can open the way to novel forms of sense-making and decision-making processes. We leverage state-of-the-art natural language processing engines for the semantic analysis of textual data sources to build a temporal graph database that highlights relationships among entities belonging to different documents and time frames. Moreover, we introduce the concept of temporal graph query that analysts can use to identify all the relationships of an entity and to visualize their evolution over time. This process enables the application of statistical algorithms that can be oriented to the automatic analysis of anomalies, state change detection, forecasting. Preliminary results demonstrate that the representation of the evolution of entities and relationships allows an analyst to highlight relevant events among the large amount of open source documents

show abstract

Techniques for Graph Analytics on Big Data

Cited by 30 publications

References 21 publications

Big Data Architecture

Big Data Architecture

DualIso: An Algorithm for Subgraph Pattern Matching on Very Large Labeled Graphs

Supporting sense-making and decision-making through time evolution analysis of open sources

Contact Info

Product

Resources

About