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Cloud computing promises high scalability, flexibility and cost-effectiveness to satisfy emerging computing requirements. To efficiently provision computing resources in the cloud, system administrators need the capabilities of characterizing and predicting workload on the Virtual Machines (VMs). In this paper, we use data traces obtained from a real data(More)
It is increasingly common to find real-life data represented as networks of labeled, heterogeneous entities. To query these networks, one often needs to identify the matches of a given query graph in a (typically large) network modeled as a target graph. Due to noise and the lack of fixed schema in the target graph, the query graph can substantially differ(More)
Uncertain, or probabilistic, graphs have been increasingly used to represent noisy linked data in many emerging application scenarios , and have recently attracted the attention of the database research community. A fundamental problem on uncertain graphs is reliability, which deals with the probability of nodes being reach-able one from another. Existing(More)
VANET is the emerging technology that is to be adopted worldwide. The studies and research for the adoption of this technology is still simulation based. VANET is a wireless adhoc networking techniques, whose feasibility and performance are usually tested by means of simulation. Routing protocols and their performances in all possible scenario of the(More)
Searching and mining large graphs today is critical to a variety of application domains, ranging from community detection in social networks, to de novo genome sequence assembly. Scalable processing of large graphs requires careful partitioning and distribution of graphs across clusters. In this paper, we investigate the problem of managing large-scale(More)
Complex social and information network search becomes important with a variety of applications. In the core of these applications, lies a common and critical problem: Given a labeled network and a query graph, how to efficiently search the query graph in the target network. The presence of noise and the incomplete knowledge about the structure and content(More)
Mining graph patterns in large networks is critical to a variety of applications such as malware detection and biological module discovery. However, frequent subgraphs are often ineffective to capture association existing in these applications, due to the complexity of isomorphism testing and the inelastic pattern definition. In this paper, we introduce(More)
We witness an unprecedented proliferation of knowledge graphs that record millions of entities and their relationships. While knowledge graphs are structure-flexible and content-rich, they are difficult to use. The challenge lies in the gap between their overwhelming complexity and the limited database knowledge of non-professional users. If writing(More)
We present GQBE, a system that presents a simple and intuitive mechanism to query large knowledge graphs. Answers to tasks such as “list university professors who have designed some programming languages and also won an award in Computer Science” are best found in knowledge graphs that record entities and their relationships. Real-world(More)