Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> Outlier detection is a well established area of statistics but most of the existing outlier detection techniques are designed for applications where the entire dataset is available for random access. A typical outlier detection technique constructs a standard data distribution or model and identifies the deviated data points from the model as outliers. Evidently these techniques are not suitable for online data streams where the entire dataset, due to its unbounded volume, is not available for random access. Moreover, the data distribution in data streams change over time which challenges the existing outlier detection techniques that assume a constant standard data distribution for the entire dataset. In addition, data streams are characterized by uncertainty which imposes further complexity. In this paper we propose an adaptive, online outlier detection technique addressing the aforementioned characteristics of data streams, called Adaptive Outlier Detection for Data Streams (A-ODDS), which identifies outliers with respect to all the received data points as well as temporally close data points. The temporally close data points are selected based on time and change of data distribution. We also present an efficient and online implementation of the technique and a performance study showing the superiority of A-ODDS over existing techniques in terms of accuracy and execution time on a real-life dataset collected from meteorological applications. <s> BIB001 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> In recent years, RFID technologies have been used in many applications, such as inventory checking and object tracking. However, raw RFID data are inherently unreliable due to physical device limitations and different kinds of environmental noise. Currently, existing work mainly focuses on RFID data cleansing in a static environment (e.g. inventory checking). It is therefore difficult to cleanse RFID data streams in a mobile environment (e.g. object tracking) using the existing solutions, which do not address the data missing issue effectively. In this paper, we study how to cleanse RFID data streams for object tracking, which is a challenging problem, since a significant percentage of readings are routinely dropped. We propose a probabilistic model for object tracking in a mobile environment. We develop a Bayesian inference based approach for cleansing RFID data using the model. In order to sample data from the movement distribution, we devise a sequential sampler that cleans RFID data with high accuracy and efficiency. We validate the effectiveness and robustness of our solution through extensive simulations and demonstrate its performance by using two real RFID applications of human tracking and conveyor belt monitoring. <s> BIB002 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> Wireless Sensor Networks (WSNs) are important and necessary platforms for the future as the concept “Internet of Things” has emerged lately. They are used for monitoring, tracking, or controlling of many applications in industry, health care, habitat, and military. However, the quality of data collected by sensor nodes is affected by anomalies that occur due to various reasons, such as node failures, reading errors, unusual events, and malicious attacks. Therefore, anomaly detection is a necessary process to ensure the quality of sensor data before it is utilized for making decisions. In this review, we present the challenges of anomaly detection in WSNs and state the requirements to design efficient and effective anomaly detection models. We then review the latest advancements of data anomaly detection research in WSNs and classify current detection approaches in five main classes based on the detection methods used to design these approaches. Varieties of the state-of-the-art models for each class are covered and their limitations are highlighted to provide ideas for potential future works. Furthermore, the reviewed approaches are compared and evaluated based on how well they meet the stated requirements. Finally, the general limitations of current approaches are mentioned and further research opportunities are suggested and discussed. <s> BIB003 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> Low quality sensor data limits WSN capabilities for providing reliable real-time situation-awareness. Outlier detection is a solution to ensure the quality of sensor data. An effective and efficient outlier detection technique for WSNs not only identifies outliers in a distributed and online manner with high detection accuracy and low false alarm, but also satisfies WSN constraints in terms of communication, computational and memory complexity. In this paper, we take into account the correlation between sensor data attributes and propose two distributed and online outlier detection techniques based on a hyperellipsoidal one-class support vector machine (SVM). We also take advantage of the theory of spatio-temporal correlation to identify outliers and update the ellipsoidal SVM-based model representing the changed normal behavior of sensor data for further outlier identification. Simulation results show that our adaptive ellipsoidal SVM-based outlier detection technique achieves better detection accuracy and lower false alarm as compared to existing SVM-based techniques designed for WSNs. <s> BIB004 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> Wireless sensor networks (WSNs) are increasingly used as platforms for collecting data from unattended environments and monitoring important events in phenomena. However, sensor data is affected by anomalies that occur due to various reasons, such as, node software or hardware failures, reading errors, unusual events, and malicious attacks. Therefore, effective, efficient, and real time detection of anomalous measurement is required to guarantee the quality of data collected by these networks. In this paper, two efficient and effective anomaly detection models PCCAD and APCCAD are proposed for static and dynamic environments, respectively. Both models utilize the One-Class Principal Component Classifier (OCPCC) to measure the dissimilarity between sensor measurements in the feature space. The proposed APCCAD model incorporates an incremental learning method that is able to track the dynamic normal changes of data streams in the monitored environment. The efficiency and effectiveness of the proposed models are demonstrated using real life datasets collected by real sensor network projects. Experimental results show that the proposed models have advantages over existing models in terms of efficient utilization of sensor limited resources. The results further reveal that the proposed models achieve better detection effectiveness in terms of high detection accuracy with low false alarms especially for dynamic environmental data streams compared to some existing models. <s> BIB005 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> In our Big Data era, data is being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Recent studies have shown that poor quality data is prevalent in large databases and on the Web. Since poor quality data can have serious consequences on the results of data analyses, the importance of veracity, the fourth ‘V’ of big data is increasingly being recognized. In this tutorial, we highlight the substantial challenges that the first three ‘V’s, volume, velocity and variety, bring to dealing with veracity in big data. Due to the sheer volume and velocity of data, one needs to understand and (possibly) repair erroneous data in a scalable and timely manner. With the variety of data, often from a diversity of sources, data quality rules cannot be specified a priori; one needs to let the “data to speak for itself” in order to discover the semantics of the data. This tutorial presents recent results that are relevant to big data quality management, focusing on the two major dimensions of (i) discovering quality issues from the data itself, and (ii) trading-off accuracy vs efficiency, and identifies a range of open problems for the community. <s> BIB006 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this paper outlines a contextual anomaly detection technique for use in streaming sensor networks. The technique uses a well-defined content anomaly detection algorithm for real-time point anomaly detection. Additionally, we present a post-processing context-aware anomaly detection algorithm based on sensor profiles, which are groups of contextually similar sensors generated by a multivariate clustering algorithm. Our proposed research has been implemented and evaluated with real-world data provided by Powersmiths, located in Brampton, Ontario, Canada. <s> BIB007 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> The ability to detect and process anomalies for Big Data in real-time is a difficult task. The volume and velocity of the data within many systems makes it difficult for typical algorithms to scale and retain their real-time characteristics. The pervasiveness of data combined with the problem that many existing algorithms only consider the content of the data source; e.g. a sensor reading itself without concern for its context, leaves room for potential improvement. The proposed work defines a contextual anomaly detection framework. It is composed of two distinct steps: content detection and context detection. The content detector is used to determine anomalies in real-time, while possibly, and likely, identifying false positives. The context detector is used to prune the output of the content detector, identifying those anomalies which are considered both content and contextually anomalous. The context detector utilizes the concept of profiles, which are groups of similarly grouped data points generated by a multivariate clustering algorithm. The research has been evaluated against two real-world sensor datasets provided by a local company in Brampton, Canada. Additionally, the framework has been evaluated against the open-source Dodgers dataset, available at the UCI machine learning repository, and against the R statistical toolbox. <s> BIB008 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> With the abundance of raw data generated from various sources, Big Data has become a preeminent approach in acquiring, processing, and analyzing large amounts of heterogeneous data to derive valuable evidences. The size, speed, and formats in which data is generated and processed affect the overall quality of information. Therefore, Quality of Big Data (QBD) has become an important factor to ensure that the quality of data is maintained at all Big data processing phases. This paper addresses the QBD at the pre-processing phase, which includes sub-processes like cleansing, integration, filtering, and normalization. We propose a QBD model incorporating processes to support Data quality profile selection and adaptation. In addition, it tracks and registers on a data provenance repository the effect of every data transformation happened in the pre-processing phase. We evaluate the data quality selection module using large EEG dataset. The obtained results illustrate the importance of addressing QBD at an early phase of Big Data processing lifecycle since it significantly save on costs and perform accurate data analysis. <s> BIB009 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> Abstract Vast and ever increasing quantities of data are produced by sensors in the Internet of Things (IoT). The quality of this data can be very variable due to problems with sensors, incorrect calibration etc. Data quality can be greatly enhanced by cleaning the data before it reaches its end user. This paper reports on the construction of a distributed cleaning system (DCS) to clean data streams in real-time for an environmental case-study. A combination of declarative and statistical model based cleaning methods are applied and initial results are reported. <s> BIB010 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> Despite advances in sensor technology, there are a number of problems that continue to require attention. Sensors fail due to low battery power, poor calibration, exposure to the elements and interference to name but a few factors. This can have a negative effect on data quality, which can however be improved by data cleaning. In particular, models can learn characteristics of data to detect and replace incorrect values. The research presented in this paper focuses on the building of models of environmental sensor data that can incorporate context awareness about the sampling locations. These models have been tested and validated both for static and streaming data. We show that contextual models demonstrate favourable outcomes when used to clean streaming data. <s> BIB011 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> To effectively clean the large-scale, mixed and inaccurate monitoring or collective data, reduce the cost of data cache and ensure the consistent deviation detection on timing data of each cycle, a big data online cleaning algorithm based on dynamic outlier detection has been proposed. The data cleaning method is improved by local outliner detection upon density, sampling cluster uniformly dilution Euclidean distance matrix retaining some corrections into next cycle of cleaning, which avoids a sampling causing overall cleaning deviation and reduces amount of calculation within data cleaning stable time, enhancing the speed greatly. Finally, the distributed solutions on online cleaning algorithm based on Hadoop platform. <s> BIB012 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> AbstractWireless Sensor Networks (WSNs) are widely used for monitoring physical happenings of the environment. However, the data gathered by the WSNs may be inaccurate and unreliable due to power exhaustion, noise and other reasons. Unnecessary data such as erroneous data and redundant data transmission causes a lot of extra energy consumption. To improve the data reliability and reduce the energy consumption, we proposed an in-network processing architecture for data cleaning, which divides the task into four stages implemented in different nodes respectively. This strategy guaranteed the cleaning algorithms were computationally lightweight in local nodes and energy-efficient due to almost no communication overhead. In addition, we presented the detection algorithms for data faults and event outliers, which were conducted by utilizing the related attributes from the local sensor node and the cooperation with its relaying neighbor. Experiment results show that our proposed approach is accurate and energy-... <s> BIB013 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> The massive growth in the scale of data has been observed in recent years being a key factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety of data that require a new high-performance processing. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. The presence of data preprocessing methods for data mining in big data is reviewed in this paper. The definition, characteristics, and categorization of data preprocessing approaches in big data are introduced. The connection between big data and data preprocessing throughout all families of methods and big data technologies are also examined, including a review of the state-of-the-art. In addition, research challenges are discussed, with focus on developments on different big data framework, such as Hadoop, Spark and Flink and the encouragement in devoting substantial research efforts in some families of data preprocessing methods and applications on new big data learning paradigms. <s> BIB014 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> The outlier detection is a popular issue in the area of data management and multimedia analysis, and it can be used in many applications such as detection of noisy images, credit card fraud detection, network intrusion detection. The density-based outlier is an important definition of outlier, whose target is to compute a Local Outlier Factor (LOF) for each tuple in a data set to represent the degree of this tuple to be an outlier. It shows several significant advantages comparing with other existing definitions. This paper focuses on the problem of distributed density-based outlier detection for large-scale data. First, we propose a Gird-Based Partition algorithm (GBP) as a data preparation method. GBP first splits the data set into several grids, and then allocates these grids to the datanodes in a distributed environment. Second, we propose a Distributed LOF Computing method (DLC) for detecting density-based outliers in parallel, which only needs a small amount of network communications. At last, the efficiency and effectiveness of the proposed approaches are verified through a series of simulation experiments. <s> BIB015 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> The data cleaning of electrical power big data can improve the correctness, the completeness, the consistency and the reliability of the data. Aiming at the difficulties of the extracting of the unified anomaly detection pattern and the low accuracy and continuity of the anomaly data correction in the process of the electrical power big data cleaning, the data cleaning model of the electrical power big data based on Spark is proposed. Firstly, the normal clusters and the corresponding boundary samples are obtained by the improved CURE clustering algorithm. Then, the anomaly data identification algorithm based on boundary samples is designed. Finally, the anomaly data modification is realized by using exponential weighting moving mean value. The high efficiency and accuracy is proved by the experiment of the data cleaning of the wind power generation monitoring data from the wind power station. <s> BIB016 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> With the fast advance of big data technology and analytics solutions, big data computing and service is becoming a very hot research and application subject in academic research, industry community, and government services. Nevertheless, there are increasing data quality problems resulting in erroneous data costs in enterprises and businesses. Current research seldom discusses how to effectively validate big data to ensure data quality. This paper provides informative discussions for big data validation and quality assurance, including the essential concepts, focuses, and validation process. Moreover, the paper presents a comparison among big data validation tools and several major players in industry are discussed. Furthermore, the primary issues, challenges, and needs are discussed. <s> BIB017 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> With the evolution of data and increasing popularity of IoT (Internet of Things), stream data mining has gained immense popularity. Researchers and developers are trying to analyze data patterns obtained from various devices. Stream data have several characteristics, the most important being its huge volume and high velocity. Although, a lot of research is being conducted in order to develop more efficient stream data mining techniques, pre-processing of stream data is an area that is under-studied. Real time applications generate data which is rather noisy and contain missing values. Apart from this, there is the issue of data evolution, which is a concern when dealing with stream data. To deal with the evolution of data, the proposed solution offers a hybrid of preprocessing techniques which are adaptive in nature. As a result of the study, an adaptive preprocessing and learning approach is implemented. The case study with sensor weather data demonstrates the results and accuracy of the proposed solution. <s> BIB018 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> Existing scalable data cleaning approaches have focused on batch data cleaning. However, batch data cleaning is not suitable for streaming big data systems, in which dynamic data is generated continuously. Despite the increasing popularity of stream-processing systems, few stream data cleaning techniques have been proposed so far. In this paper, we bridge this gap by addressing the problem of rule-based stream data cleaning, which sets stringent requirements on latency, rule dynamics and ability to cope with the continuous nature of data streams. We design a system, called Bleach, which achieves real-time violation detection and data repair on a dirty data stream. Bleach relies on efficient, compact and distributed data structures to maintain the necessary state to repair data. Additionally, it supports rule dynamics and uses a "cumulative" sliding window operation to improve cleaning accuracy. We evaluate a prototype of Bleach using both synthetic and real data streams and experimentally validate its high throughput, low latency and high cleaning accuracy, which are preserved even with rule dynamics. <s> BIB019 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> ABSTRACTNowadays technological advances have promoted big data streams common in many applications, including mobile internet applications, internet of things, and industry production process. Outl... <s> BIB020 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> With the rapid development of social networks, Internet of things, Cloud computing as well as other technologies, big data age is arriving. The increasing number of data has brought great value to the public and enterprises. Meanwhile how to manage and use big data better has become the focus of all walks of life. The 4V characteristics of big data have brought a lot of issues to the big data processing. The key to big data processing is to solve data quality issue, and to ensure data quality is a prerequisite for the successful application of big data technique. In this paper, we use recommendation systems and prediction systems as typical big data applications, and try to find out the data quality issues during data collection, data preprocessing, data storage and data analysis stages of big data processing. According to the elaboration and analysis of the proposed issues, the corresponding solutions are also put forward. Finally, some open problems to be solved in the future are also raised. <s> BIB021 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> With the development of Smart Grid Technology, more and more electric power sensor data are utilized in various electric power systems. To guarantee the effectiveness of such systems, it is necessary to ensure the quality of electric power sensor data, especially when the scale of electric power sensor data is large. In the field of large-scale electric power sensor data cleaning, the computational efficiency and accuracy of data cleaning are two vital requirements. In order to satisfy these requirements, this paper presents an electric power sensor data oriented data cleaning solution, which is composed of a data cleaning framework and a data cleaning method. Based on Hadoop, the given framework is able to support large-scale electric power sensor data acquisition, storage and processing. Meanwhile, the proposed method which achieves outlier detection and reparation is implemented on the basis of a time-relevant k-means clustering algorithm in Spark. The feasibility and effectiveness of the proposed method is evaluated on a data set which originates from charging piles. Experimental results show that the proposed data cleaning method is able to improve the data quality of electric power sensor data by finding and repairing most outliers. For large-scale electric power sensor data, the proposed data cleaning method has high parallel performance and strong scalability. <s> BIB022 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> The concept of Top-N local outlier that focuses on the detection of the N points with the largest Local Outlier Factor (LOF) score has been shown to be very effective for identifying outliers in big datasets. However, detecting Top-N local outliers is computationally expensive, since the computation of LOF scores for all data points requires a huge number of high complexity k-nearest neighbor (kNN) searches. In this work, we thus present the first distributed solution to tackle this problem of Top-N local outlier detection (DTOLF). First, DTOLF features an innovative safe elimination strategy that efficiently identifies dually-safe points, namely those that are guaranteed to (1) not be classified as Top-N outliers and (2) not be needed as neighbors of points residing on other machines. Therefore, it effectively minimizes both the processing and communication costs of the Top-N outlier detection process. Further, based on the well-accepted observation that strong correlations among attributes are prevalent in real world datasets, we propose correlation-aware optimization strategies that ensure the effectiveness of grid-based partitioning and of the safe elimination strategy in multi-dimensional datasets. Our extensive experimental evaluation on OpenStreetMap, SDSS, and TIGER datasets demonstrates the effectiveness of DTOLF — up to 10 times faster than the alternative methods and scaling to terabyte level datasets. <s> BIB023 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> Since a low-quality data may influence the effectiveness and reliability of applications, data quality is required to be guaranteed. Data quality assessment is considered as the foundation of the promotion of data quality, so it is essential to access the data quality before any other data related activities. In the electric power industry, more and more electric power data is continuously accumulated, and many electric power applications have been developed based on these data. In China, the power grid has many special characteristic, traditional big data assessment frameworks cannot be directly applied. Therefore, a big data framework for electric power data quality assessment is proposed. Based on big data techniques, the framework can accumulate both the real-time data and the history data, provide an integrated computation environment for electric power big data assessment, and support the storage of different types of data. <s> BIB024 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> Abstract Big data changed the way in which we collect and analyze data. In particular, the amount of available information is constantly growing and organizations rely more and more on data analysis in order to achieve their competitive advantage. However, such amount of data can create a real value only if combined with quality: good decisions and actions are the results of correct, reliable and complete data. In such a scenario, methods and techniques for the Data Quality assessment can support the identification of suitable data to process. If for traditional database numerous assessment methods are proposed, in the Big Data scenario new algorithms have to be designed in order to deal with novel requirements related to variety, volume and velocity issues. In particular, in this paper we highlight that dealing with heterogeneous sources requires an adaptive approach able to trigger the suitable quality assessment methods on the basis of the data type and context in which data have to be used. Furthermore, we show that in some situations it is not possible to evaluate the quality of the entire dataset due to performance and time constraints. For this reason, we suggest to focus the Data Quality assessment only on a portion of the dataset and to take into account the consequent loss of accuracy by introducing a confidence factor as a measure of the reliability of the quality assessment procedure. We propose a methodology to build a Data Quality adapter module, which selects the best configuration for the Data Quality assessment based on the user main requirements: time minimization, confidence maximization, and budget minimization. Experiments are performed by considering real data gathered from a smart city case study. <s> BIB025 </s> Big Data Quality: A systematic literature review and future research directions <s> Future research directions <s> The combination of data and technology is having a high impact on the way we live. The world is getting smarter thanks to the quantity of collected and analyzed data. However, it is necessary to consider that such amount of data is continuously increasing and it is necessary to deal with novel requirements related to variety, volume, velocity, and veracity issues. In this paper we focus on veracity that is related to the presence of uncertain or imprecise data: errors, missing or invalid data can compromise the usefulness of the collected values. In such a scenario, new methods and techniques able to evaluate the quality of the available data are needed. In fact, the literature provides many data quality assessment and improvement techniques, especially for structured data, but in the Big Data era new algorithms have to be designed. We aim to provide an overview of the issues and challenges related to Data Quality assessment in the Big Data scenario. We also propose a possible solution developed by considering a smart city case study and we describe the lessons learned in the design and implementation phases. <s> BIB026
Despite the advances of big data quality over the past few years, some problems remain unsolved. Here is a list of these problems as well as future directions, which are described below.  Using hybrid methods, achieving higher accuracy: Due to the popularity of social networks and similar data sources that generate data continuously, quality assessment should be done in an online manner to allow the analyst to make the right decision in real time. On the other hand, in the batch processing, due to the large amount of stored data, the focus of the most methods is to use the common tools, such as Hadoop, Spark and etc. to perform their processing at the right time. Therefore, in these methods, the technique used does not have much innovation. Furthermore, if in hybrid methods, loading data and constructing the model have been done at the right time, these type of processing can be an appropriate option for evaluating data in real time because as indicated in BIB018 , BIB019 , they are more accurate than other methods.  Distributed architecture, more efficient but less used: In many studies, centralized architecture is used, while due to the high volume of data and also considering the computational constraints and providing an appropriate solution to the fault tolerant problem, researchers should use a distributed architecture. It is stated in BIB005 ,  , BIB009 , BIB013 , BIB003 ,  - BIB020 , BIB014 , BIB021 , BIB006 that distributed architecture can be a good solution to the high communication cost, moreover, studies BIB004 , BIB010 , BIB011 , BIB022 , BIB012 - BIB007 , BIB015 , BIB023 , BIB019 , BIB024 use distributed architecture in order to face this problem. However, distributed architecture also has challenges such as node failure, network interruption, and so forth, which should be an excellent solution to these problems.  Self-adaptive, a way to recognize the concept of drift: Some papers BIB005 - BIB002 , BIB001 , BIB025 , BIB026 , BIB018 presented an adaptive solution for assessing big data quality and BIB003 has emphasized on the presentation of an adaptive algorithm. As discussed earlier, data distribution may change over time; in this situation, the algorithm should adapt itself to the changes in the distribution of data. If the proposed method fails to predict the data distribution and do not consider the adaptation, it is difficult to detect outliers or evaluate the data, so the proposed method should be adaptable.  Context awareness, capable of detecting global deviations instead of local: In order to increase the accuracy of the assessment, relying on local data is not enough, and global data should be considered in addition to the local data. For example, if in a sensor network, the data produced by a specific sensor is poor quality, it may be due to the failure of that sensor. In this situation, if assessment algorithm only considers the data of each sensor individually, we will never reach the correct result, while comparing the sensor's data with other sensors, we can understand the environment events and evaluate more accurately. Due to the small number of context aware studies BIB008 -  , BIB025 and the importance of this topic as well as its higher accuracy, it is recommended that researchers use this technique to evaluate the big data.  Human intervention, better accuracy in batch, less velocity in stream: In some papers, the expert intervenes to adjust some of the parameters to improve the accuracy of the method. In some studies BIB016 , BIB003 ,  this problem has been mentioned, in addition, the intervention of the expert for the type of batch processing in which real-time processing is not critical is appropriate, but this is a severe concern for real-time processing. The proposed method should either adopt a mechanism to minimize this intervention or take this process in the shortest possible time so as not to impede the fast processing.  Domain specific, missing point in studies: Although selecting a specific domain and setting the corresponding parameters in the algorithm can help to improve its accuracy, most of the proposed methods are domain-general. Providing a general approach that does not fit into specific areas is not the right solution for domains such as health, social network, and other domains, each has its characteristics.  Related dataset, significant for method evaluation: Using an appropriate dataset in order to test the proposed algorithm is one of the essential tasks that should be done in any related research work. The lack of appropriate dataset for specific domains is one of the most important challenges in data quality assessment. While in some of the presented methods, synthetic datasets are used; in others, the datasets used are either unavailable or not very relevant to the domain.  Quality dimensions, high variety and choice difficulty: As indicated in Figure 6 and the second layer, the task of the paper is shown which one of them is an evaluation. All studies in this path have some quality dimensions that are essential for evaluating data quality. Given that different quality models are presented, the proper selection of quality dimensions should be proportional to the domain, because inappropriate selection and evaluation may lead to waste of time and precision.  Lack of suitable tools: Another problem that has not been solved yet is providing a proper tool for assessing the big data quality. As indicated in BIB017 , most commonly used tools only assess the quality of batch data and do not focus on the data stream, while real-time data evaluation is one of the severe needs of this area. Also, these tools after profiling the data only consider the accuracy and completeness dimensions and have no idea to evaluate other quality dimensions, such as timeliness, consistency and so on.