Graph databases (GDB) have recently been arisen to overcome the limits of traditional databases for storing and managing data with graph-like structure. Today, they represent a requirementfor many applications that manage graph-like data,like social networks.Most of the techniques, applied to optimize queries in graph databases, have been used in traditional databases, distribution systems,… or they are inspired from graph theory. However, their reuse in graph databases should take care of the main characteristics of graph databases, such as dynamic structure, highly interconnected data, and ability to efficiently access data relationships. In this paper, we survey the query optimization techniques in graph databases. In particular,we focus on the features they have introduced to improve querying graph-like data.
In this paper, we propose an approach, which is based on web usage mining techniques, to recommend webviews to be materialized. The webview materialization is a term used to represent the transformation of dynamic web data into equivalent static web data. That is the creation of a static instance of a dynamic web page, at a certain point in time. In this work we will extend our previous approach [6] which concerns the use of sequential patterns to recommend the materialized webviews. Firstly we analyze the DIWS historic to extract the frequent sequential patterns and the frequent association rules. Then we use these repetitive behaviors to calculate a materialization weight for each webview. The webviews with high materialization weights are the most recommended for the materialization. Our experiment results show that our approach reduces the materialization risk more than those using only the recent time period to select the materialized webviews.
Climate change has become global issue for crops due to increase in temperature and less rainfall resulting in shortage of water and decrease in yield. The experiment consisted of five guar (Cyamopsis tetragonoloba L.) genotypes viz. S-5744, S-5824, S-5785, BR-90, BR-99 were cultivated under irrigated and drought stress (only soaking dose) conditions using Randomized Complete Block Design (RCBD) with 3 replications. The data for yield and its components were recorded at the time of maturity of guar genotypes. The two genotypes S-5744 and S-5824 showed better performance under both drought and irrigated conditions and gave maximum yield 900 and 1133.33 grams in drought and irrigated, respectively. All characters showed positive correlation with each other except number of cluster plant -1 . The soil analysis was done in order to check availability and utilization of nutrients in the presence of water (irrigated conditions) and water deficit conditions (drought). The above mentioned two genotypes were less affected by the water shortage compared to others and were comeback after rainfall in the months of July and August and have ability of better uptake of nutrients from the soil in drought and irrigated conditions. However, nutrient use was higher under irrigated conditions.
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