Approximate query processing can help reduce response time for aggregate queries in exploratory data analysis. In this study, we describe basic query transformation rules for processing approximate queries using synthetic data tables or inferential models. Based on the preliminary experimental results, we confirm that ML models can be used to provide approximate query results in response times acceptable for applications.

Huge amounts of data are ceaselessly being generated by a variety of devices, and the processing efforts for their collection and analysis grows exponentially as well. Storing them in one place and getting exact answers is almost impractical. Furthermore, computing aggregation and statistics that most exploratory data analysis would require imposes a heavy burden on networking and computing infrastructures. By adopting the edge/fog computing paradigm that has recently been developing can reduce such overheads by offloading jobs from central clouds to edge devices. We try to go one step further in this direction by approximating aggregate values and statistics for data analysis using tractable probabilistic models and optimizing network performance. This paper evaluates our preliminary result of our on-going project that was gained by fast-prototyping using Sum-Product Networks.

Recommendation systems are a popular marketing strategy for online service providers. These systems predict a customer's future preferences from the past behaviors of that customer and the other customers. Most of the popular online stores process millions of transactions per day; therefore, providing quick and quality recommendations using the large amount of data collected from past transactions can be challenging. Parallel processing power of GPUs can be used to accelerate the recommendation process. However, the amount of memory available on a GPU card is limited; thus, a number of passes may be required to completely process a large-scale dataset. This paper proposes two parallel, item-based recommendation algorithms implemented using the CUDA platform. Considering the high sparsity of the user-item data, we utilize two compression techniques to reduce the required number of passes and increase the speedup. The experimental results on synthetic and real-world datasets show that our algorithms outperform the respective CPU implementations and also the naïve GPU implementation which does not use compression.

The skyline queries are often used in several recommendation applications. Most existing related works have focused on skyline computation in many multidimensional data. However, these works do not consider an interesting query generated from non-skyline point. In this paper, we propose a new query, called skyline minimum vector which finds the minimum vector for making a non-skyline point into a skyline. The skyline minimum vector means the minimum cost for becoming a skyline. We use the Manhattan distance between skyline and query point in order to evaluate the cost. Also, we propose basic algorithm and optimized algorithm for getting skyline minimum vector. The proposed query will be very useful in many decision-making applications.