Abstract
Database queries are one of the most important functions of a relational database. Users are interested in viewing a variety of data representations, and this may vary based on database purpose and the nature of the stored data. The Air Force Institute of Technology has approximately 100 data logs which will be converted to the standardized Scorpion Data Model format. A relational database is designed to house this data and its associated sensor and non-sensor metadata. Deterministic polynomial-time queries were used to test the performance of this schema against two other schemas, with databases of 100 and 1000 logs of repeated data and randomized metadata. Of these approaches, the one that had the best performance was chosen as AFIT’s database solution, and now more complex and useful queries need to be developed to enable filter research. To this end, consider the combined Multi-Objective Knapsack/Set Covering Database Query. Algorithms which address The Set Covering Problem or Knapsack Problem could be used individually to achieve useful results, but together they could offer additional power to a potential user. This paper explores the NP-Hard problem domain of the Multi-Objective KP/SCP, proposes Genetic and Hill Climber algorithms, implements these algorithms using Java, populates their data structures using SQL queries from two test databases, and finally compares how these algorithms perform.
Highlights
Big Data is a field that has been receiving a lot of attention in recent years
Due to the complexity of the Problem Domain (PD) and the recorded success of Genetic Algorithm (GA) used with Multiobjective Optimization Problem (MOP), this paper proposes a GA stochastic population based search algorithm and a Hill Climber (HC) stochastic local search algorithm to return answers to the combined Knapsack Problem (KP)/Set Covering Problem (SCP) PDs
The Sensor Metadata table is coupled with the channelInfo table so that specific sensors can be affiliated with channels for a given mission, and the Scorpion Data Model (SDM) Data tables are comprised of the data collected by those sensors
Summary
Big Data is a field that has been receiving a lot of attention in recent years. While a definitive definition is elusive, a definition which provides context is that big data is “the datasets that could not be perceived, acquired, managed, and processed by traditionalIT and software/hardware tools within a tolerable time” [1]. AFIT has approximately 100 data logs ( called missions) which will be stored in this relational database, and this number is expected to grow annually. This database requires a number of queries from parties interested in using this data for research. This section covers some specific details of the database design in order to motivate how the SCP and KP can combine to help facilitate database queries. This database is implemented in PostgreSQL, which is an open source, object-relational database system which dates back to 1986 at the University of California at Berkely. The Sensor Metadata table is coupled with the channelInfo table so that specific sensors can be affiliated with channels for a given mission, and the SDM Data tables are comprised of the data collected by those sensors
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