GraphZero

Subgraph matching is a fundamental task in many applications which identifies all the embeddings of a query pattern in an input graph. Compilation-based subgraph matching systems generate specialized implementations for the provided patterns and often substantially outperform other systems. However, the generated code causes significant computation redundancy and the compilation process incurs too much overhead to be used online, both due to the inherent symmetry in the structure of the query pattern. In this paper, we propose an optimizing query compiler, named GraphZero, to completely address these limitations through symmetry breaking based on group theory. GraphZero implements three novel techniques. First, its schedule explorer efficiently prunes the schedule space without missing any high-performance schedule. Second, it automatically generates and enforces a set of restrictions to eliminate computation redundancy. Third, it generalizes orientation, a surprisingly effective optimization that was only used for clique patterns, to apply to arbitrary patterns. Evaluation on multiple query patterns shows that GraphZero outperforms two state-of-the-art compilation and non-compilation based systems by up to 40X and 2654X, respectively.

Using children’s search patterns to predict the quality of their creative problem solving

Purpose The purpose of this paper is to propose a computational model that implicitly predict the children’s creative quality of solutions by analyzing the query pattern on a problem-solving-based lesson. Design/methodology/approach A search task related to the competencies acquired in the classroom was applied to automatically measure children’ creativity. A blind review process of the creative quality was developed of 255 primary school students’ solutions. Findings While there are many creativity training programs that have proven effective, many of these programs require measuring creativity previously which involves time-consuming tasks conducted by experienced reviewers, i.e. far from primary school classroom dynamics. The authors have developed a model that predicts the creative quality of the given solution using the search queries pattern as input. This model has been used to predict the creative quality of 255 primary school students’ solutions with 80 percent sensitivity. Research limitations/implications Although the research was conducted with just one search task, participants come from two different countries. Therefore, the authors hope that this model provides detection of non-creative solutions to enable prompt intervention and improve the creative quality of solutions. Originality/value This is the first implicit classification model of query pattern in order to predict the children’ creative quality of solutions. This model is based on a conceptual relation between the concept association of creative thinking and query chain model of information search.

Retrieval of Data from Very Large Databases Using Apriori Algorithm

Now-a-days most of the Very Large Databases like Medical Databases, Multimedia Database are consuming Petabytes of memory. For extracting required data from this enormous amount of memory so many retrieval techniques were already developed. But all these techniques are time consuming. We can also use indexing supported by all the commercial databases. When we create an index internally it uses B-Tree data structure which is used for efficient retrieval of data. But, Oracle10g is supporting R-Tree indexes also which are more efficient than B-Tree indexes. On Very Large Databases it is not sufficient to create a single index because these Databases consist of large number of fields. Moreover, it is not necessary to create index on each and every field and it is also not possible because it requires large memory area. In very large databases also most of the times the user concentrates on few fields only. So, in our proposed system, the solution is to create some suggested indexes. And as and when the user’s query pattern changes the suitable index from the suggested index set is dynamically loaded into the main memory from the local storage. Our proposed technique continuously monitors the query patterns and it creates the suggested indexes using Apriori algorithm and this suggested index set is updated periodically based on the query pattern changes.