The integration of high data transmission rates and the recent digital multimedia technology, paves the way to access a huge amount of video over the internet, in seconds. Additionally, uploading videos to different websites is no more confined to expert software professionals resulting in duplication of video data which led to exorbitant growth of multimedia information in cyberspace in a short span of time. This necessitates the development of efficient data management techniques including storage, searching and annotation mechanism. Automatic shot boundary detection is considered to be the first and foremost step towards such management. It is a booming area of research gaining attention in the domain of image processing, computer vision and pattern recognition. In this review paper, we present a detailed description of the methods and algorithms of shot boundary detection, reported in the last two decades. This review shows that using multiple features performs well in comparison to using only a single feature in the shot boundary detection problem although it leads to higher complexity. The major sources of disturbance in the boundary detection are the sudden illumination variation and presence of high motion in the video. An adaptive threshold outperforms a single global threshold in the boundary detection problem and the threshold requirement can be avoided through learning based strategies at the cost of larger training data and higher computation time. Moreover the present review includes a critical analysis of relative merits and demerits of existing algorithms and finally opens promising research directions in the area.

In the last few years, the field of quantum computing has experienced remarkable progress. The prototypes of quantum computers already exist and have been made available to users through cloud services (e.g., IBM Q experience, Google quantum AI, or Xanadu quantum cloud). While fault-tolerant and large-scale quantum computers are not available yet (and may not be for a long time, if ever), the potential of this new technology is undeniable. Quantum algorithms have the proven ability to either outperform classical approaches for several tasks, or are impossible to be efficiently simulated by classical means under reasonable complexity-theoretic assumptions. Even imperfect current-day technology is speculated to exhibit computational advantages over classical systems. Recent research is using quantum computers to solve machine learning tasks. Meanwhile, the database community has already successfully applied various machine learning algorithms for data management tasks, so combining the fields seems to be a promising endeavour. However, quantum machine learning is a new research field for most database researchers. In this tutorial, we provide a fundamental introduction to quantum computing and quantum machine learning and show the potential benefits and applications for database research. In addition, we demonstrate how to apply quantum machine learning to the join order optimization problem in databases.

The join order is one of the most important factors that impact the speed of query processing. Its optimization is known to be NP-hard, such that it is worth investigating the benefits of utilizing quantum computers for optimizing join orders. Hence in this paper, we propose to model the join order problem as quadratic unconstrained binary optimization (QUBO) problem to be solved with various quantum approaches like QAOA, VQE, simulated annealing and quantum annealing. In this paper, we will show the enhanced performance using our QUBO formulation to find valid and optimal shots for real-world queries joining three, four and five relations on D-Wave quantum annealer. The highlight of this study will demonstrate that we were able to generate valid and optimal join orders for five relations using the D-Wave quantum annealer, with values of about 12% and 0.27% respectively and we were able to find the optimal bushy join trees. Experiments for the gate-based simulations were able to produce optimized join order for three and four relations including bushy trees using QAOA and VQE algorithms.

The optimization of queries speeds up query processing in databases. One of the most time-consuming tasks in query processing is the join operation, where the order of the joins plays a crucial role in determining the number of tuples to be processed for intermediate results, and hence, the overall processing costs. In this paper, we use a variational quantum circuit (VQC) to create a hybrid classical-quantum machine learning algorithm to predict efficient join orders by learning from past join orders. We develop an encoding of the join order problem using a low number of qubits. We show that VQCs with filtering of cross joins outperform the classical dynamic programming optimizer of PostgreSQL with a 2.7% faster execution time.