27571789
Computation of intraprocedural dynamic program slices
Abstract Dynamic slicing algorithms are used in interactive applications such as program debugging and testing. Therefore, these algorithms need to be very efficient. In this context, we propose three intraprocedural dynamic slicing algorithms which are more space and time efficient than the existing algorithms. Two of the proposed algorithms compute precise dynamic slices of structured programs using Program Dependence Graph as an intermediate representation. To compute precise dynamic slices of unstructured programs, we introduce the concepts of jump dependence and Unstructured Program Dependence Graph. The third algorithm uses Unstructured Program Dependence Graph as the intermediate program representation, and computes precise dynamic slices of unstructured programs. We show that each of our proposed algorithms is more space and time efficient than the existing algorithms.
16820690
Introducing Local Optimization for Effective Initialization and Crossover of Genetic Decision Trees
We introduce a new genetic operator, Reduction, that recties decision trees not correct syntactically and at the same time removes the redundant sections within, while preserving its accuracy during operation. A novel approach to crossover is presented that uses the reduction operator to systematically extract building blocks spread out over the entire second parent to create a subtree that is valid and particularly useful in the context it replaces the subtree in the rst parent. The crossover introduced also removes unexplored code from the ospring and hence prevents redundancy and bloating. Overall, reduction can be viewed as a local optimization step that directs the population, generated initially or over generations through crossovers, to potentially good regions in the search space so that reproduction is performed in a highly correlated landscape with a global structure. Lexical convergence is also ensured implying identical individuals always produce the same ospring.
500117
A probabilistic relational model and algebra
Although the relational model for databases provides a great range of advantages over other data models, it lacks a comprehensive way to handle incomplete and uncertain data. Uncertainty in data values, however, is pervasive in all real-world environments and has received much attention in the literature. Several methods have been proposed for incorporating uncertain data into relational databases. However, the current approaches have many shortcomings and have not established an acceptable extension of the relational model. In this paper, we propose a consistent extension of the relational model. We present a revised relational structure and extend the relational algebra. The extended algebra is shown to be closed, a consistent extension of the conventional relational algebra, and reducible to the latter.
28618041
Availability of a periodically inspected system, maintained under an imperfect-repair policy
A periodically inspected system is maintained through a fixed number of imperfect-repairs before being replaced or perfectly repaired. The lifetime distribution of the system in its new and imperfectly repaired states is arbitrary. The times required for imperfect repairs and for perfect repair or replacement are random. The repaired system is restored to operation at the next scheduled inspection time. The system availability function is expressed as the weighted average of the survival functions and their shifts, and gives the limiting average availability.
19572588
Web Site Personalization Using User Profile Information
In this paper we discuss a technique for web site personalization. Connectivity analysis has been shown to be useful in identifying high quality web pages within a topic or domain specific graph of hyper linked documents. We have implemented a system that creates a view of a subset of a web site most relevant to a given user. This sort of personalization is useful for filtering a useful subset of a site so that the user gets a low volume of quality information. The essence of our approach is to augment a previous connectivity analysis with content analysis. We present an agent which assists the user when he browses and distills a personalized sub graph of the website based on his user profile.
14335537
A Data Perturbation Approach to Privacy Protection in Data Mining
Advances in data mining techniques have raised growing concerns about privacy of personal information. Organizations that use their customers’ records in data mining activities are forced to take actions to protect the privacy of the individuals involved. A common practice for many organizations today is to remove the identity-reated attributes from customer records before releasing them to data miners or analysts. In this study, we investigate the effect of this practice and demonstrate that a majority of the records in a dataset can be uniquely identified even after identity related attributes are removed. We propose a data perturbation method that can be used by organizations to prevent such unique identification of individual records, while providing the data to analysts for data mining. The proposed method attempts to preserve the statistical properties of the data based on privacy protection parameters specified by the organization. We show that the problem can be solved in two phases, with a linear programming formulation in phase one (to preserve the marginal distribution), followed by a simple Bayes-based swapping procedure in phase two (to preserve the joint distribution). The proposed method is compared with a random perturbation method in classification performance on two real-world datasets. The results of the experiments indicate that it significantly outperforms the random method.
4723885
Learning while solving problems in best first search
We investigate the role of learning in search-based systems for solving optimization problems. We use a learning model, where the values of a set of features can be used to induce a clustering of the problem state space. The feasible set of h* values corresponding to each cluster is called h*set. If we relax the optimality guarantee, and tolerate a risk factor, the distribution of h*set can be used to expedite search and produce results within a given risk of suboptimality. The off-line learning method consists of solving a batch of problems by using A* to learn the distribution of the h*set in the learning phase. This distribution can be used to solve the rest of the problems effectively. We show how the knowledge acquisition phase can be integrated with the problem solving phase. We present a continuous online learning scheme that uses an "anytime" algorithm to learn continuously while solving problems.