To contribute in filling up the semantic gap in data warehouses and OLAP data cubes, and enable semantic exploration and reasoning on them, this paper highlights the need for semantically augmenting Geo/BI data with convenient semantic relations, and provides OWL-based ontologies (ODW and OOLAP) which are capable of replicating data warehouses (respectively OLAP data cubes) in the form semantic data with respect of Geo/BI data structures, and which enable the possibility of augmenting these semantic BI data with semantic relations. Moreover, the paper demonstrates how ODW and OOLAP ontologies can be combined to current Geo/BI data structures to deliver either pure semantic Geo/BI data or mixed semantically interrelated Geo/BI data to business professionals.

The computer memory has been revolutionized in the last 25-30 years, in terms of both capacity and speed of execution. Along with this, even the logic controlling the memory has also become more and more complex and difficult to interface. Usually, memory subsystems will be designed to interact with a single system. Whenever we consider a two system is sharing a common memory, there comes the need for an Arbiter. The major difference between a memory arbiter and a processor scheduler is that the memory arbiter works at a much finer level of granularity. The time taken for the task execution may range from micro to milliseconds, while a RAM controller needs to serve the request in a few nanoseconds. Because of this reason the resource arbiters are usually designed and implemented in hardware rather than in software.

The development of wireless technology has facilitated the wide deployment of mobile communication systems. The beauty of wireless communication is that all nooks and corners can be reached at a cheaper and faster rate when compared with wireline. Wireless is now dominating the telecommunications market. Initially, the dawn of wireless was seen as the dawn of communications to poor countries and rural areas which were poorly covered by wireline devices due to high cost. Currently, the story has changed. Both the wired and unwired environments are clamoring for wireless connectivity. Considering the hype of R&D in broadband technologies and easy acceptance in the market place, wireline communications may soon die a natural death. However, wireless communications faces a few challenges. One of them is that the radio frequency (RF) carrier signals used in these communication systems degrades as it travels through the air interface due to attenuation and interference. As a result, the range of coverage may not be as planned leading to very weak reception or even dead zones where no communication can be done. This problem has resulted in the development of cellular signal boosters that help in receiving the weak signal, amplifying and then re-transmitting it to reach the uncovered areas. Boosters are now giving hope to the frustrated wireless users such as indoor users and those at the fringes of a cell site. These boosters are diverse in make, range, method of operation, deployment and cost. In this paper, a survey of various signal booster designs, deployment and performance is presented. It is hoped that this will serve as a one-stop shop for researchers and developers in the important field of wireless signal boosters and extenders, who wish to know what is available and existing challenges.

Evaluation of water reuse options is also one of the applications of multi-criteria decision-making (MCDM) problems. In this paper, we refer to a new method for selecting the best water reuse option in the available options by using picture fuzzy MCDM.

The business sectors directly contribute to the growth of any nation. Moreover, the business is an activity of producing, buying, and selling the goods and services to generate the money. The business directly involves in the gross domestic product (GDP). The business forecasting is the activity of predicting or estimating the feature position of the sales, expenditures, and profits of any business. However, the business forecasting helps to the business sectors for planning, decision making, resource utilization, business success, etc. Therefore, business forecasting is a pressing need for the growth of any business. In recent past, many researches attempt to carry out the business forecasting using different tools. However, this paper presents the business forecasting for sales data using machine learning technique and the obtained results are presented and discussed..

Mobile phones are one of the highly used gadgets now a day. These handheld devices serve multiple purposes through different available functionalities. Demand of services and functionalities vary with time and person concern. Before purchasing a new mobile phone, one has to judge specifications like functionalities, hardware capabilities and efficiencies available with the particular model of the device. We often find it difficult to identify or decide the best model among the available multiple alternatives by heuristics quick analysis of the specifications and prices.
This paper proposes a method for ranking mobile phone models based on Analytical Hierarchical Process (AHP), one of the typically used mathematical models for Multi Criteria Decision Making (MCDM) problems. The effectiveness of the proposed method is analyzed through a case study consisting of various sophisticated approaches based on AHP. A novice mobile phone buyer will be benefitted by the use of the proposed method incorporated through e-commerce sites.