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A logical schema design for a non-relational database based on the theory of data dependency and the strategy of decomposing data into a single document is presented. This approach is based on the efficiency of using a Document Oriented database that implements the logical data modeling and the design principle modes of a typical NoSQL Database. Logical data modeling is done by identifying the underlying complex data and de-normalizing the data into query output. This creates a complex schema but stores it in such a way that the data type is the same as application programming data types, which is helpful in retrieving data. Schema design patterns are proposed for efficient schema design to avoid challenges as the data volume increases. Best practices in indexing strategies to handle web data are also highlighted. This data modeling helps save storage space and time during and after development, which eventually improves performance throughout the application's lifetime. [ABSTRACT FROM AUTHOR]

The recommendation systems become very important aspect in all areas like products ,movies , socials media and etc. Recommendation suggested from more overloaded information and analyze has to be done on massive amount of data. Apart from that media product like movie recommendation needs more process on previous and current data to suggest the user choice. In this paper we proposed a movie recommendation system based on the content and history based approach. This model starts the process by implementing of user similarity matrix. On the matrix results we applied the KNN to maps the user data to movie data based on the use choice. We also implemented the cosine similarity function between user choice to item data to find similarity and non similarity data outputs. This approach of movie recommendation system will do the process on history and content and produces the optimal results. [ABSTRACT FROM AUTHOR]

Sarcasm is a mode of communication where people express themselves by writing it's contrary to what they really say. Sarcasm is a general phenomenon in social media. It is very difficult to identify and analyze in text. And also complex task because of challenges involved in determining the essence of the text containing sarcasm. The major difficulty in sarcasm detection is Ambiguity in nature. Sarcasm detection is very simple while face to face voice conversation based on pitch of tone, facial expressions, and gestures of opposite person. But in text conversation it is very difficult because sarcasm is conveying negative meaning with positive words. Sarcasm Detection difficult by human because if its challenges, so researchers have been working on this for the past decade. In this paper we discussed about approaches to detect sarcasm, types of sarcasm and issues involved in sarcasm detection. [ABSTRACT FROM AUTHOR]

Economy of any country is investment in stock market. Stock price can be affected by factors which are running in current world and stock market. One of the most important things is to predict the how stock market will do. In stock market prediction machine learning has significant applications. Accurate stock market predication results are biggest challenge, because financial stock markets are volatile in nature. Forecasting stock market price is one of the most popular research areas for researchers. In this article, we had designed a model to predict the stock price of a particular company by analysing its previous data i.e. previous stock prices. We had analysed around 1300 stock prices of company, find the graph of company and created a model to predict present stock price. In this paper, we discussed about what is stock market prediction, how to predict with machine learning, analysed on some set of data and present results of our analysis. [ABSTRACT FROM AUTHOR]

IoT based Fog Computing become major needy technology integration for innovative engineering. In the IoT based technologies the Fog nodes communicate with network edge node and creates easy interaction with Cloud data centers. The data which generated from the origin points transmit to destination in the network of different devices. In each device to process and execute the task needs some energy consumption. In this paper we proposed a Cost Function for Energy (CFE) which evaluate the total energy consumption during in transmission of data from source to destination based on the task. The total energy consumption depended on the total delay of task execution which presents amount of time for execution of a task. The CFE calculated for every tasks execution at local and other nodes of network. In our proposal we have included the greedy heuristic approach for evaluate the total delay accordingly the energy consumption. [ABSTRACT FROM AUTHOR]