This project is a Recommender System for OLAP Reports, KPI’s,Scorecards and Business Dashboards.OLAP (Online Analytical Processing) is a powerful technology which performs multidimensional analysis of business data and lays a foundation to many kinds of business applications like Business Performance Management, Planning, Budgeting, Forecasting, Financial Reporting, Analysis, Simulation Models, Knowledge Discovery, and Data Warehouse Reporting. OLAP enables end-users to perform ad hoc analysis of data in multiple dimensions, thereby providing the insight and understanding they need for better decision making.
However It is very time consuming and requires lot of research and ground work to come up with the list of Business Questions, Metrics, KPI’s, Scorecards,Reports and Dashboards , Report Layouts etc. Our proposed framework does all the groundwork at back end and displays the proposed list of KPI’s, Reports and Dashboards to each user based on his Past History, Data Mart Accessibility, Frequently Accessed Reports and Seasonal Trends etc. It also provides Ranking to Metrics and KPI’s as per Users History. Users can easily select from the proposed list and access the reports then and there.
Keywords— OLAP, Online Analytical Processing, Data-warehousing Reports and Dashboard, Business Analytics,KPI’s, Scorecards, Key Performance Indicators, Decision Making Analytics, Recommender System for OLAP Reports

In the current generation, all the users right from the end user to sophisticated or advanced user, as per their needs,   there is a great demand for various kinds of services like data service, voice service, application service etc., more on the context based instead of infrastructure based service.  Next Generation Networks provides all the user required services with a good strategy of maintaining the services and management of services. This paper focuses on the management of these services offered in Next Generation Networks in a systematic and dynamic to all the users.

Many universities are involved in projects related to the design, assembly and operation of fast fading channel to increase the experience level of researchers and students. For high speed data communication, the recovering data operation from the noisy data corrupted with inter-symbol interference (ISI), the equalizers are necessary components of the receiver architecture. Fading is one of main problems which affect on the transmitted signal through medium have many obstacles. In this medium the signal suffers from three phenomena: reflection, refraction and scattering. The fading come from these phenomena’s defined as multipath fading. The fast fading is used to describe channels when the coherence time of the channel < transmission symbol time. The superimposed training sequence is one modern technical employed to decrease the effect of multipath fading channel. The aim of this paper is to investigate and compare the effect of using superimposed training sequence technique instead of the conventional training sequence (CTS) in OFDM systems for three kinds of modulation schema (BPSK,QPSK and 16QAM) to meet the fast fading Rayleigh channel, caused by multipath, requirements, based on the use of RLS adaptive equalizer.

Keywords— Fast Fading Channel, Recursive Least Square Equalizer, Superimposed Training Sequence STS.

Mobile Ad Hoc Networks (MANETs) use many different routing protocols to route data packets between the nodes. Efficient routing mechanism is a challenging task for group oriented computing in Mobile Ad Hoc Networks (MANETs).  A MANET is a wireless mobile network that is self forming, self maintained and self healing. In MANET’s network nodes stay connected even as the network topology changes. The ability of MANETs to support adequate Quality of Service (QoS) for group communication is limited by the ability of the underlying ad-hoc routing protocols to provide consistent behavior despite the dynamic properties of mobile computing devices. A number of ad hoc routing protocols have been developed during the time, but none of these is able to produce efficient routing of packets in large number of nodes due to their own limitations. This Paper presents the performance of two routing protocols  OLSR(Optimized link state routing protocol) and DSR(Dynamic source routing protocol) using metrics throughput, packets delivery ratio and End-to-end delay. The performance evaluation of routing protocols is done by using two different traffics i.e TCP and UDP. Our Simulation tool will be NS-2.

Keywords – MANET, OLSR, DSR ,TCP,UDP

We view the cloud as a marketplace for trading instances of web services, which can be “leased” by web applications. We argue that the application can “buy” diversity by selecting instances of web services from multiple cloud sellers in this market. The approach portfolio theory to construct a diversified portfolio of web service instances, which are traded from multiple cloud providers. With the growing rate of data used in applying the storage space along with the price for maintaining the data storage increases, thus everyone move towards cloud domain, hence an effective platform is needed for the purchase of the cloud domain and satisfy the following condition, Test the approach effectiveness in minimizing the risk of SLA violation. Simulate the dynamic and adaptive behaviour of the approach in responding to changes in the market conditions and risk
Evaluate the sensitivity of the allocation decisions to risk and its correlation with other candidates. Evaluate the scalability of the approach and its ramifications on risk reduction under extreme scenarios. We use a portfolio based algorithm which satisfies the above condition and helps the user to get the required web service with the fluctuating changes in the market without changing the SLA.
Selection of cloud-traded services is a relatively new area. However, none of the approaches have considered the problem of diversity in web services, selection through trading and portfolio thinking. The current web services allocation solutions in the cloud market.