A Study of Mongodb and Oracle in an E-Commerce Environment

As worldwide e-commerce expands, businesses continue to look for better ways to meet their evolving needs with web solutions that scale and perform adequately. Several online retailers have been able to address scaling challenges through the implementation of NoSQL databases. While architecturally different from their relational database counterparts, NoSQL databases typically achieve performance gains by relaxing one or more of the essential transaction processing attributes of atomicity, consistency, isolation, and durability (ACID). As with any emerging technology, there are both critics and supporters of NoSQL databases. The detractors claim that NoSQL is not safe and is at a greater risk for data loss. On the other hand, its ardent defenders boast the performance gains achieved over their relational counterparts. This thesis studies the NoSQL database known as MongoDB, and discusses its ability to support the growing needs of e-commerce data processing. It then examines MongoDB\u27s raw performance (compared to Oracle 11g R2, a relational database) and discusses its adherence to ACID

AGRICULTURAL LAND CONVERSION IN THE TWIN CITIES: PART II, THE NATIONAL RESOURCES INVENTORY

We divided the thirteen-county Twin Cities Metropolitan Statistical Area into a core and a fringe of seven and six counties, respectively. The National Resources Inventory estimates that 170 thousand acres of the Core were converted from agriculture to other uses between 1982 and 1987, while only about 46 thousand acres of the Fringe were so converted. The conversion rate was much greater in the Core than on the Fringe according to the NRIbut not according to the Census of Agriculture. The number of acres of agricultural land converted for each new resident ranged from 0.15 in Sherburne County to 2.49 in Pierce County. Viewed another way, the increase in urban land to house new residents ranged from 0.28 in Ramsey County to 1.23 acres per person in Isanti County.Land Economics/Use,

Activity Recognition for Quality Assessment of Batting Shots in Cricket using a Hierarchical Representation

Quality assessment in cricket is a complex task that is performed by understanding the combination of individual activities a player is able to perform and by assessing how well these activities are performed. We present a framework for inexpensive and accessible, automated recognition of cricketing shots. By means of body-worn inertial measurement units, movements of batsmen are recorded, which are then analysed using a parallelised, hierarchical recognition system that automatically classifies relevant categories of shots as required for assessing batting quality. Our system then generates meaningful visualisations of key performance parameters, including feet positions, attack/defence, and distribution of shots around the ground. These visualisations are the basis for objective skill assessment thereby focusing on specific personal improvement points as identified through our system. We evaluated our framework through a deployment study where 6 players engaged in batting exercises. Based on the recorded movement data we could automatically identify 20 classes of unique batting shot components with an average F1-score greater than 88%. This analysis is the basis for our detailed analysis of our study participants’ skills. Our system has the potential to rival expensive vision-based systems but at a fraction of the cost