Big Data Quality: A systematic literature review and future research directions <s> Evaluation Task <s> Big Data is often referred to as the 3Vs: Volume, Velocity and Variety. A 4th V (validity) was introduced to address the quality dimension. Poor data quality can be costly, lead to breaks in processes and invalidate the company's efforts on regulatory compliance. In order to process data streams in real time, a new technology called CEP (complex event processing) was developed. In France, the current deployment of smart meters will generate massive electricity consumption data. In this work, we developed a diagnostic approach to compute generic quality indicators of smart meter data streams on the fly. This solution is based on Tibco StreamBase CEP. Visualization tools were also developed in order to give a better understanding of the inter-relation between quality issues and geographical/temporal dimensions. According to the application purpose, two visualization methods can be loaded: (1) StreamBase LiveView is used to visualize quality indicators in real time; and (2) a Web application provides a posteriori and geographical analysis of the quality indicators which are plotted on a map within a color scale (lighter colors indicate good quality and darker colors indicate poor quality). In future works, new quality indicators could be added to the solution which can be applied in an operational context in order to monitor data quality from smart meters. <s> BIB001 </s> Big Data Quality: A systematic literature review and future research directions <s> Evaluation Task <s> Abstract The use of sensors has had an enormous increment in the last years, becoming a valuable tool in many different areas. In this kind of scenario, the quality of data becomes an extremely important issue; however, not much attention has been paid to this specific topic, with only a few existing works that focus on it. In this paper, we present a proposal for managing data streams from sensors that are installed in patients’ homes in order to monitor their health. It focuses on processing the sensors’ data streams, taking into account data quality. In order to achieve this, a data quality model for this kind of data streams and an architecture for the monitoring system are proposed. Moreover, our work introduces a mechanism for avoiding false alarms generated by data quality problems. <s> BIB002
The explanation of the papers with the task of evaluating the data stream is presented in this section. The big data evaluation methods are mentioned in this section. These methods are divided into two categories: schema and instance based. In six papers, the schema has been used to evaluate big data, and in four papers, authors have used the data itself for this purpose. First, we explain the instance-based papers. There are four papers in this section that assess data quality on both batch and streaming data. The interpretation of each method is as follows. A data quality assessment tool for power supply sources is presented in BIB001 . In the tool provided, streaming data from the XML format is taken from the smart meter and evaluated online and stored for later analysis in the Oracle database. Given the fact that the power data feature is already specified, for each feature, some indicators and quality rules are defined. After calculating quality indicators, whether online or offline, the results are presented in real time and graphically. The most important quality indicators are checking missing values, duplicated data and comparing the value with a threshold. The framework has been implemented, and there are screenshots of the tool in the paper, but the accuracy and runtime of the tool have not been evaluated. As the processing of a large amount of data is complicated, most of the methods for assessing the quality of big data use sampling. In  , after data sampling, the data quality, in particular, the completeness, is evaluated for a small amount of data. As the data is likely to be updated, their quality may also change. For example, due to updating, the data that was complete is now null, and vice versa, the data that did not have any values is now a complete record. Two approaches are presented here. First, by observing any updates, all selected data are reevaluated. It is clear that this method can be both time-consuming and resource-intensive. However, the proposed method reevaluates only the records that have been changed. The runtime and efficiency of the method have been evaluated, which results show a 78% efficiency of the proposed method. To control the patient's health, his condition is monitored by checking the patient's data generated by sensors that are connected to the patient. In BIB002 , the information is received from the body sensors and sensors in the patient's house room, and after assessing the quality of the data, if it is at risk level, an alarm is generated. The proposed architecture has three main components. The first component is monitoring in which the requirements and quality parameters are defined, and these requirements are sent to the Middleware component. After receiving data from different sensors, this component manages and executes requirements for data. The Middleware component interacts with the Data Quality Manager component. The Data Quality Manager is responsible for assessing data quality and also analyzes the measured values by examining relevant historical data. If the measured values do not match with the minimum and maximum historical values, the alarms are generated. The proposed method is part of the thesis and implementation, and evaluation has not been presented in the paper. It can be said that in this thesis  statistical information, metadata, etc. from the static data is obtained from a database and then the quality assessment is applied to streaming data. Firstly, the author uses three algorithms such as reference algorithm, a frequency algorithm, and an entropy algorithm to identify essential data in the database. Secondly, there is a model for quality assessment that has five layers and can support the evaluation of data steam quality. Finally, a distributed platform is developed with Python language, and the author compares the results in terms of the algorithm's performance in a machine and parallel and the results show that the higher the number of threads, the less time it takes to process.