Quantum computers for optimization the performance

Computers decrease human work and concentrate on enhancing the performance to advance the technology. Various methods have been developed to enhance the performance of computers. Performance of computer is based on computer architecture, while computer architecture differs in various devices, such as microcomputers, minicomputers, mainframes, laptops, tablets, and mobile phones. While each device has its own architecture, the majority of these systems are built on Boolean algebra. In this study, a few basic concepts used in quantum computing are discussed. It is known that quantum computers do not possess any transistor and chip while being roughly 100 times faster than a common classic silicon computer. Scientists believe that quantum computers are the next generation of the classic computers

Identifying skylines in cloud databases with incomplete data

Skyline queries is a rich area of research in the database community. Due to its great benefits, it has been integrated into many database applications including but not limited to personalized recommendation, multi-objective, decision support and decision-making systems. Many variations of skyline technique have been proposed in the literature addressing the issue of handling skyline queries in incomplete database. Nevertheless, these solutions are designed to fit with centralized incomplete database (single access). However, in many real-world database systems, this might not be the case, particularly for a database witha large amount of incomplete data distributed over various remote locations such as cloud databases. It is inadequate to directly apply skyline solutions designed for the centralized incomplete database to work on cloud due to the prohibitive cost. Thus, this paper introduces a new approach called Incomplete-data Cloud Skylines (ICS) aiming at processing skyline queries in cloud databases with incomplete data. This approach emphasizes on reducing the amount of data transfer and domination tests during skyline process. It incorporates sorting technique that assists in arranging the data items in a way where dominating data items will be placed at the top of the list helping in eliminate dominated data items. Besides, ICS also employs a filtering technique to prune the dominated data items before applying skyline technique. It comprises a technique named local skyline joiner that helps in reducing the amount of data transfer between datacenters when deriving the final skylines. It limit the amount of data items to be transferred to only those local skylines of each relation. A comprehensive experiment have been performed on both synthetic and real-life datasets, which demonstrate the effectiveness and versatility of our approach in comparison to the current existing approaches. We argue that our approach is practical and can be adopted in many contemporary cloud database systems with incomplete data to process skyline queries

Developing context-aware mobile applications using composition process based-on heterogeneous software entities

Despite the tremendous number of mobile applications (apps) that developed using various implementation forms such as component, service, or app, user’s needs are unlike each other. Besides, mobile devices are characterized by heterogeneous software and hardware configurations. Developing customized mobile applications needs to explore and incorporate new entities in the surrounding user context. Besides, involving the existing heterogeneous entities might benefit in developing context-aware mobile apps. Thus, a significant challenge in the development process of mobile apps is the deployment of these applications in the heterogeneous devices available on the market. To tackle these challenges, there is a need for a composition process to reuse and utilize the existing heterogeneous entities to develop mobile apps according to user’s requirements. Hence, the behavior of the desired apps can be customized according to the user context information. This paper addresses the issue of discovering, integrating and reusing the existing heterogeneous software entities in developing a customized mobile application. In this paper we propose framework for context-aware mobile apps composition process based-on existing heterogeneous software entities

Reporting skyline on uncertain dimension with query interval

Naturally, users sometimes specify their preference in an imprecise way (i.e. query with an interval/range). To report results that satisfy the imprecise query as well as interesting would be easy on dataset with atomic values. The challenge is when the dataset being queried consists of both atomic values as well as continuous range of values. For a set of objects with uncertain dimension and given a query interval [qi,qi'] on that uncertain dimension, a skyline query on that interval returns the objects which are not dominated by any other objects in the query interval. A method is proposed to help determine objects that intersect with the query interval and answer skyline query that satisfy the query interval. The correctness of the method is proven through comparisons between two methods that strictly reject and loosely accept objects from/into the query interval

Identifying skylines in dynamic incomplete database

Nowadays in database systems finding the best results that meet the preferences of users is the most important issue. Skyline queries will present the data items that are not being dominated by the other items in a database. Most of the operations assume the database is complete which means there are no missing values in the database dimensions. In reality, databases are not complete especially for multidimensional database. Missing values have a negative effect on finding skyline points. It changes the native of dominance relation, leads to cyclic dominance and unsatisfying the transitivity property of skylines. This problem becomes more severe in dynamic database in which new items are inserted or items are deleted or updated from the database. Besides, most of the works that handled the incomplete issue assumed that items are static. In this paper we propose the new approach which finds the most relevant data items that meet user’s preferences for dynamic incomplete databases