Graph pattern matching is typically defined in terms of subgraph isomorphism, which makes it an np-complete problem. Moreover, it requires bijective functions, which are often too restrictive to characterize patterns in emerging applications. We propose a class of graph patterns, in which an edge denotes the connectivity in a data graph within a predefined number of hops. In addition, we define matching based on a notion of bounded simulation, an extension of graph simulation. We show that with this revision, graph pattern matching can be performed in cubic-time, by providing such an algorithm. We also develop algorithms for incrementally finding matches when data graphs are updated, with performance guarantees for dag patterns. We experimentally verify that these algorithms scale well, and that the revised notion of graph pattern matching allows us to identify communities commonly found in real-world networks.

Graph Pattern Matching.

A survey of evolutionary computation for association rule mining

The recent state of the art innovations in technology enables the development of low-cost sensor nodes with processing and communication capabilities. The unique characteristics of these low-cost sensor nodes such as limited resources in terms of processing, memory, battery, and lack of tamper resistance hardware make them susceptible to clone node or node replication attack. The deployment of WSNs in the remote and harsh environment helps the adversary to capture the legitimate node and extract the stored credential information such as ID which can be easily re-programmed and replicated. Thus, the adversary would be able to control the whole network internally and carry out the same functions as that of the legitimate nodes. This is the main motivation of researchers to design enhanced detection protocols for clone attacks. Hence, in this paper, we have presented a systematic literature review of existing clone node detection schemes. We have also provided the theoretical and analytical survey of the existing centralized and distributed schemes for the detection of clone nodes in static WSNs with their drawbacks and challenges.

/pdf/a-systematic-review-on-clone-node-detection-in-static-50pbgowhmd.pdf

A Systematic Review on Clone Node Detection in Static Wireless Sensor Networks

Combining Apriori heuristic and bio-inspired algorithms for solving the frequent itemsets mining problem

An efficient humanitarian inventory control model and emergency logistics system plays a crucial role in maintaining reliable flow of vital supplies to the victims located in the shelters and minimizing the impacts of the unforeseen disruptions that can occur. This system should not only allow the efficient usage and distribution of emergency supplies but should also offer the ability to be integrated with emerging ITS technologies such as Radio Frequency Identification Devices (RFIDs) for commodity tracking and logistics. This paper proposes a comprehensive methodology for the development of a humanitarian emergency management framework based on the real-time tracking of emergency supplies and demands through the use of RFID technology integrated with a multi-commodity stochastic humanitarian inventory management model (MC-SHIC). First, logistics and management aspects of RFID technologies in the context of the emergency disaster relief framework are discussed. Then, MC-SHIC model proposed to determine the optimal emergency inventory levels to prevent possible disruptions at the minimal cost is presented. The solution of the model with several sensitivity analyses obtained using the pLEPs algorithm is presented and discussed. Realizing that actual emergency inventory levels can deviate from optimal values during the actual disaster relief period due to the possible stochastic disruptions such as fluctuating demand for vital supplies in the shelters, a comprehensive on-line inventory control framework is proposed to minimize impacts of these unforeseen disruptions, or at least to address the problem at hand as fast as possible. Within this methodology, we obtain an approximation of the MC-SHIC model using a simultaneous perturbation stochastic approximation (SPSA) based functional approximator, and compare the performance of these algorithms for solving the new unconstrained optimization problem. Finally, proposed model-free on-line control methodology is discussed using examples to understand the efficiency and practicality of both algorithms in terms of computational times and accuracy of results.

/pdf/a-secure-and-efficient-inventory-management-system-for-gn0s4wt41m.pdf

A secure and efficient inventory management system for disasters

Association rules mining (ARM) is a well-known combinatorial optimization problem aiming at extracting relevant rules from given large-scale datasets. According to the state of the art, the bio-inspired methods proved their efficiency by generating acceptable solutions in a reasonable time when dealing with small and medium size instances. Unfortunately, to cope with large instances such as the webdocs benchmark, these methods require more and more powerful processors and are time expensive. Nowadays, computing power is no longer a real issue. It can be provided by the power of emerging technologies such as graphics processing units (GPUs) that are massively multi-threaded processors. In this paper, we investigate the use of GPUs to speed up the computation. We propose two GPU-based bees swarm algorithms for association rules mining (single evaluation in GPU, SE-GPU and multiple evaluation in GPU, ME-GPU). SE-GPU aims at evaluating one rule at a time where each thread is associated with one transaction, whereas ME-GPU evaluates multiple rules in parallel on GPU where each thread is associated with several transactions. To validate our approaches, the two algorithms have been executed to solve well-known large ARM instances. Real experiments have been carried out on an Intel Xeon 64 bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. The results show that our approaches improve the execution time up to 100$$\times $$× over the sequential mono-core bees swarm optimization-ARM algorithm. Moreover, the proposed approaches have been compared with CPU multi-core ones (1---8 cores). The results show that they are faster than the multi-core versions whatever is the number of used cores.

GPU-based bees swarm optimization for association rules mining

There are numerous projects dealing with disaster management and emergency response that use wireless sensor networks technologies. Indeed, WSNs offer a good alternative compared to traditional ad hoc networks. Air pollution monitoring, forest fire detection, landslide detection, natural disaster prevention, industrial sense and control applications, dangerous gas leakage, water level monitoring, vibration detection to prevent an earthquake, radiation monitoring are examples of the WSN applications related to disaster management. This paper presents an overview of the recent projects using WSN to collect data in disaster areas.

Disaster Management Projects Using Wireless Sensor Networks: An Overview

A review on security challenges of wireless communications in disaster emergency response and crisis management situations

Besides its NP-completeness, the strict constraints of subgraph isomorphism are making it impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed GPM models have emerged as they yield interesting results in a polynomial time. However, massive graphs generated by mostly social networks require a distributed storing and processing of the data over multiple machines, thus, requiring GPM to be revised by adopting new paradigms of big graphs processing, e.g., Think-Like-A-Vertex and its derivatives. This article discusses and proposes a classification of distributed GPM approaches with a narrow focus on the relaxed models.

/pdf/a-survey-on-distributed-graph-pattern-matching-in-massive-2hjcaug19s.pdf

A Survey on Distributed Graph Pattern Matching in Massive Graphs

This paper addresses the problem of association rules mining with large data sets using bees behaviors. The bees swarm optimization method have been successfully applied on small and medium data size. Nevertheless, when dealing Webdocs benchmark (the largest benchmark on the web), it is bluntly blocked after more than 15 days. Additionally, Graphic processor Units are massively threaded providing highly intensive computing and very usable by the optimization research community. The parallelization of such method on GPU architecture can be deal large data sets as the case of WebDocs in real time. In this paper, the evaluation process of the solutions is parallelized. Experimental results reveal that the suggested method outperforms the sequential version at the order of ×100 in most data sets, furthermore, the WebDocs benchmark is handled with less than ten hours

Nadia Nouali-Taboudjemat

Papers

GPU-based bees swarm optimization for association rules mining

Disaster Management Projects Using Wireless Sensor Networks: An Overview

A review on security challenges of wireless communications in disaster emergency response and crisis management situations

A Survey on Distributed Graph Pattern Matching in Massive Graphs

Parallel association rules mining using GPUS and bees behaviors