Many users searching databases through the web in various domains like vehicles, real estate, etc. One of the predicament, we observed in this task is ranking the results retrieved from a database for a user query. The contemporary methods are addressing this problem by sorting the database values. A common line in these methods is that ranking is done in a user and query-independent manner i.e. there is no correspondence between different users queries. We proposed a query and user-dependent approach for ranking query results in relational databases, which is articulating the opinion expressed in query by different users. The designed and developed methodology shows that the ranking of the data in database is constituted depending on the various user opinions i.e. access of tuple through users query expression. The design is based on counting the access of database tuple by users query(ies)
while many financiers use the Internet and social media to help them with investment decisions, these online tools can provide many benefits for investors and at the same time, same tools can make smart objectives for lawbreakers. These offenders are quick to adapt to new technologies-and Social media is no exception. Social media, such as Facebook, YouTube, Twitter, and LinkedIn, have become key tools for investors worldwide. Whether they are seeking study on particular stocks, background information on a broker-dealer or investment consultant, guidance on an overall investment strategy, up to date news or to simply want to the markets with others, investors turn to social media. Social media also offers a number of features that criminals may find attractive. Fraudsters can use social media in their efforts to appear legitimate, to hide behind anonymity, and to reach many people at low cost.
Internet of Things (IoT) paradigm is progressing at an enormous rate creating an expansive ecosystem of interconnected smart devices which needs to be controlled and managed by end applications or users. So, the solution is to discover the best device matching the user requirement in timely manner. Ensuring efficient and context aware resource discovery is a challenge in seamless operation of IoT applications. Semantic Intelligence can be built for resource discovery. In this paper, we propose semantic enrichment of COAP protocol to discover the resources. Resources are semantically modelled, and COAP protocol CORE Link format is modified to incorporate semantic information. Also, we have proposed a framework which is aware of the social relationship between the devices to accommodate distributed topologies. We conducted the experiments in COOJA simulator on CONTIKI OS with COAP protocol semantically enriched. COAP CORE Link format and Resource Directory were semantically enriched, and its effectiveness was measured.
Hardware and Software technology has undergone a sea-of-change in recent past. Hardware technology has moved from single-core to multi-core machine, thus capable of executing multi-task at the same time. But traditional software’s (Legacy system) are still in use today in business world. It is not easy to replace them with new software system as they carry loads of knowledge, business value with them. Also, to build new software system by taking the requirements afresh involves lot of resources in terms of skilled human resources, time and financial resources. At last the customer may not have confidence in this new software. Instead of building a new software, an attempt is made to develop a semi-automated methodology by learning about the program itself (machine learning about the program) to abstract the independent modules present in the same abstraction level (implementation level) and recode the legacy program (single threaded program) into multi-threaded parallel program. A case study program is considered and execution time is noted and analyzed for both the original program and reengineered program on a multi-core machine.
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