Computer Science & Information Technology 11th International Conference on Security and its Applications (CNSA 2018) January 2-3, 2018, Zurich, Switzerland
The 11th International Conference on Security and its Applications (CNSA 2018) was held in Zurich, Switzerland, during January 02~03, 2018. The 5th International Conference on Data Mining and Database (DMDB 2018) and The 5th International Conference on Artificial Intelligence and Applications (AIAP 2018) was collocated with The 11th International Conference on Security and its Applications (CNSA 2018). The conferences attracted many local and international delegates, presenting a balanced mixture of intellect from the East and from the West. The goal of this conference series is to bring together researchers and practitioners from academia and industry to focus on understanding computer science and information technology and to establish new collaborations in these areas. Authors are invited to contribute to the conference by submitting articles that illustrate research results, projects, survey work and industrial experiences describing significant advances in all areas of computer science and information technology.
Day by day data is increasing, and most of the data stored in a database after manual transformations and derivations. Scientists can facilitate data intensive applications to study and understand the behaviour of a complex system. In a data intensive application, a scientific model facilitates raw data products to produce new data products and that data is collected from various sources such as physical, geological, environmental, chemical and biological etc. Based on the generated output, it is important to have the ability of tracing an output data product back to its source values if that particular output seems to have an unexpected value. Data provenance helps scientists to investigate the origin of an unexpected value. In this paper our aim is to find a reason behind the unexpected value from a database using query inversion and we are going to propose some hypothesis to make an inverse query for complex aggregation function and multiple relationship (join, set operation) function.