Mining Frequent Sequential Rules with An Efficient Parallel Algorithm

Sequential rule mining is one of the most common data mining techniques. It intends to find desired rules in large sequence databases. It can decide the essential information that helps acquire knowledge from large search spaces and select curiously rules from sequence databases. The key challenge is to avoid wasting time, which is particularly difficult in large sequence databases. This paper studies the mining rules from two representations of sequential patterns to have compact databases without affecting the final result. In addition, execute a parallel approach by utilizing multi core processor architecture for mining non-redundant sequential rules. Also, perform pruning techniques to enhance the efficiency of the generated rules. The evaluation of the proposed algorithm was accomplished by comparing it with another non-redundant sequential rule algorithm called Non-Redundant with Dynamic Bit Vector (NRD-DBV). Both algorithms were performed on four real datasets with different characteristics. Our experiments show the performance of the proposed algorithm in terms of execution time and computational cost. It achieves the highest efficiency, especially for large datasets and with low values of minimum support, as it takes approximately half the time consumed by the compared algorithm.

An efficient procedure for mining egocentric temporal motifs

Temporal graphs are structures which model relational data between entities that change over time. Due to the complex structure of data, mining statistically significant temporal subgraphs, also known as temporal motifs, is a challenging task. In this work, we present an efficient technique for extracting temporal motifs in temporal networks. Our method is based on the novel notion of egocentric temporal neighborhoods, namely multi-layer structures centered on an ego node. Each temporal layer of the structure consists of the first-order neighborhood of the ego node, and corresponding nodes in sequential layers are connected by an edge. The strength of this approach lies in the possibility of encoding these structures into a unique bit vector, thus bypassing the problem of graph isomorphism in searching for temporal motifs. This allows our algorithm to mine substantially larger motifs with respect to alternative approaches. Furthermore, by bringing the focus on the temporal dynamics of the interactions of a specific node, our model allows to mine temporal motifs which are visibly interpretable. Experiments on a number of complex networks of social interactions confirm the advantage of the proposed approach over alternative non-egocentric solutions. The egocentric procedure is indeed more efficient in revealing similarities and discrepancies among different social environments, independently of the different technologies used to collect data, which instead affect standard non-egocentric measures.

Program analysis via efficient symbolic abstraction

This paper concerns the scalability challenges of symbolic abstraction: given a formula ϕ in a logic L and an abstract domain A , find a most precise element in the abstract domain that over-approximates the meaning of ϕ. Symbolic abstraction is an important point in the space of abstract interpretation, as it allows for automatically synthesizing the best abstract transformers. However, current techniques for symbolic abstraction can have difficulty delivering on its practical strengths, due to performance issues. In this work, we introduce two algorithms for the symbolic abstraction of quantifier-free bit-vector formulas, which apply to the bit-vector interval domain and a certain kind of polyhedral domain, respectively. We implement and evaluate the proposed techniques on two machine code analysis clients, namely static memory corruption analysis and constrained random fuzzing. Using a suite of 57,933 queries from the clients, we compare our approach against a diverse group of state-of-the-art algorithms. The experiments show that our algorithms achieve a substantial speedup over existing techniques and illustrate significant precision advantages for the clients. Our work presents strong evidence that symbolic abstraction of numeric domains can be efficient and practical for large and realistic programs.

High performance modified bit-vector based packet classification module on low-cost FPGA

The packet classification plays a significant role in many network systems, which requires the incoming packets to be categorized into different flows and must take specific actions as per functional and application requirements. The network system speed is continuously increasing, so the demand for the packet classifier also increased. Also, the packet classifier's complexity is increased further due to multiple fields should match against a large number of rules. In this manuscript, an efficient and high performance modified bitvector (MBV) based packet classification (PC) is designed and implemented on low-cost Artix-7 FPGA. The proposed MBV based PC employs pipelined architecture, which offers low latency and high throughput for PC. The MBV based PC utilizes <2% slices, operating at 493.102 MHz, and consumes 0.1 W total power on Artix-7 FPGA. The proposed PC considers only 4 clock cycles to classify the incoming packets and provides 74.95 Gbps throughput. The comparative results in terms of hardware utilization and performance efficiency of proposed work with existing similar PC approaches are analyzed with better constraints improvement.

Interpolating bit-vector formulas using uninterpreted predicates and Presburger arithmetic

The inference of program invariants over machine arithmetic, commonly called bit-vector arithmetic, is an important problem in verification. Techniques that have been successful for unbounded arithmetic, in particular Craig interpolation, have turned out to be difficult to generalise to machine arithmetic: existing bit-vector interpolation approaches are based either on eager translation from bit-vectors to unbounded arithmetic, resulting in complicated constraints that are hard to solve and interpolate, or on bit-blasting to propositional logic, in the process losing all arithmetic structure. We present a new approach to bit-vector interpolation, as well as bit-vector quantifier elimination (QE), that works by lazy translation of bit-vector constraints to unbounded arithmetic. Laziness enables us to fully utilise the information available during proof search (implied by decisions and propagation) in the encoding, and this way produce constraints that can be handled relatively easily by existing interpolation and QE procedures for Presburger arithmetic. The lazy encoding is complemented with a set of native proof rules for bit-vector equations and non-linear (polynomial) constraints, this way minimising the number of cases a solver has to consider. We also incorporate a method for handling concatenations and extractions of bit-vector efficiently.