Abstract
Data for police and crime analysis is becoming large and complex and increasing the difficulty for technological implementations particularly storage and retrieval mechanisms. Crimes are committed each day which demand for data engineering techniques that are flexible enough to handle complex formats and high volumes of data. NoSql technologies are typically used efficient implementations of data with processing of increasingly large volumes of cases and related data. This further helps in processing unstructured data, and providing rapid processing times to users. However, it is always a challenge to decide on the type of NoSql database for crime data. In this paper, we evaluated two NoSQL database technologies: Cassandra and MongoDB for storage and retrieval of homicide dataset. Initially, we developed a mechanism to store these datasets in Cassandra and MongoDB followed by a systematic implementation of test and evaluation criteria for read, write, and update scenarios. The experiment results were analyzed and compared and from the results, it can be concluded that Cassandra overthrows MongoDB in efficiency, reliability, and consistency.
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Askew, R., Tirumala, S.S., Anjan Babu, G. (2019). Evaluating Big Data Technologies for Statistical Homicide Dataset. In: Kalita, J., Balas, V., Borah, S., Pradhan, R. (eds) Recent Developments in Machine Learning and Data Analytics. Advances in Intelligent Systems and Computing, vol 740. Springer, Singapore. https://doi.org/10.1007/978-981-13-1280-9_8
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DOI: https://doi.org/10.1007/978-981-13-1280-9_8
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