Stream Processing Languages in the Big Data Era

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 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

The real-time analysis of Big Data streams is a terrific resource for transforming data into value. For this, Big Data technologies for smart processing of massive data streams are available, but the facilities they offer are often too raw to be effectively exploited by analysts. RAM3S (Real-time Analysis of Massive MultiMedia Streams) is a framework that acts as a middleware software layer between multimedia stream analysis techniques and Big Data streaming platforms, so as to facilitate the implementation of the former on top of the latter. RAM3S has been proven helpful in simplifying the deployment of non-parallel techniques to streaming platforms, such as Apache Storm or Apache Flink. In this paper, we show how RAM3S has been updated to incorporate novel stream processing platforms, such as Apache Samza, and to be able to communicate with different message brokers, such as Apache Kafka. Abstracting from the message broker also provides us with the ability to pipeline several RAM3S instances that can, therefore, perform different processing tasks. This represents a richer model for stream analysis with respect to the one already available in the original RAM3S version. The generality of this new RAM3S version is demonstrated through experiments conducted on three different multimedia applications, proving that RAM3S is a formidable asset for enabling efficient and effective Data Mining and Machine Learning on multimedia data streams.

In this work a new approach to the construction of models and logic circuits of algorithms and procedures for information technology processing and analysis of big data streams within the fractal paradigm is describes. In this case, data streams are defined as information objects whose physical nature can be arbitrary. The information object is investigated apart from any model or scheme, the logical scheme of intellectual technology is built in the form of: facts, regularities and reality. Fractal methods form the framework of the logical, algorithmic and content essence of the approach. The basic premise of the approach is as follows. First, the processing and analysis of data stream to determine whether it forms a fractal structure and construct a phase portrait of data stream as an information object. Second, to distinguish the areas of fractal percolation and aggregation in the multifractal structure of the stream, the phase portrait is used. Third, to estimate the spatial and temporal scales of fractal percolation and aggregation processes.

As many commercial businesses aspire for a competitive edge, real-time analysis on streaming data employing a big data methodology has lately become widespread. The ability to efficiently arrange massive amounts of data to make a business decision empowers data warehousing. Dealing with this sort of data poses considerable obstacles; as a result, several ways of assessing data in the form of streams have been created. Many solutions for dealing with enormous volumes of data and making decisions based on off-line batch processing have been investigated. In this paper, we explore the most recent developments in big data approaches for real-time analysis on streaming data to answer four research questions related to streaming data source, stream pre-processing, types of streaming data processing, used machine learning model, in addition to the validation and evaluation criteria. The system architecture associated with the used big data tools, the architecture hardware specs as well as other current platforms appropriate for large data streaming analytics are also considered. Furthermore, we outline numerous difficulties encountered in big data stream processing. The purpose of this review is to fill a gap in the surveyed area by offering thorough evaluation of various big data frameworks, architectures, and categories for cutting-edge approaches, as well as critical analyses of their performance and discussions of their applications, trends, and future directions to serve as guides for readers in this burgeoning study area.

Summary form only given. Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Their sheer volume and speed pose a great challenge for the data mining community to mine them. Data streams demonstrate several unique properties: infinite length, concept-drift, concept-evolution, and feature-evolution. Concept-drift occurs in data streams when the underlying concept of data changes over time. Concept-evolution occurs when new classes evolve in streams. Feature-evolution occurs when feature set varies with time in data streams. Each of these properties adds a challenge to data stream mining. This invited talk will present an organized picture on how to handle various data mining techniques in data streams: in particular, how to handle classification in evolving data streams by addressing these challenges.

EDITORIAL article Front. Pediatr., 05 November 2015Sec. Neonatology Volume 3 - 2015 | https://doi.org/10.3389/fped.2015.00095

Processing high-volume, high-velocity data streams is an important big data problem in many sciences, engineering, and technology domains. There are many open-source distributed stream processing and cloud platforms that offer low-latency stream processing at scale, but the visualization and user-interaction components of these systems are limited to visualizing the outcome of stream processing results. Visual analysis represents a new form of analysis where the user has more control and interactive capabilities either to dynamically change the visualization, analytics or data management processes. VAStream provides an environment for big data stream processing along with interactive visualization capabilities. The system environment consists of hardware and software modules to optimize streaming data workflow (that includes data ingest, pre-processing, analytics, visualization, and collaboration components). The system environment is evaluated for two real-time streaming applications. The real-time event detection using social media streams uses text data arriving from sources such as Twitter to detect emerging events of interest. The real-time river sensor network analysis project uses unsupervised classification methods to classify sensor network streams arriving from the US river network to detect water quality problems. We discuss implementation details and provide performance comparison results of various individual stream processing operations for both stream processing applications.

This paper presents an integrated approach to real-time processing and analysis of large-scale data streams using artificial intelligence methods, multi-agent systems, and multi-layered control architectures. Particular attention is devoted to combining technologies that are traditionally applied separately: unmanned aerial vehicle (UAV) control systems, situational centers, streaming video services, and distributed sensor networks. The proposed model accounts for the stochastic nature of the environment, the dynamic behaviour of data flows, and resource constraints, which are critical factors for real-time systems. The study develops a multi-level multi-agent architecture that includes central, regional, and edge nodes, as well as local agents such as drones, sensors, and client-side video devices. A mathematical formalization of agent interaction and reward functions is proposed to ensure a balance between service quality, latency, packet loss, and energy consumption. The use of multi-agent reinforcement learning (MARL) algorithms is introduced to support adaptive decision-making in real time, along with the development of a digital twin of the environment for predicting future system states using deep and generative models. The results demonstrate that the integration of edge computing, hierarchical decision-making, and local agent autonomy significantly enhances system resilience, provides stable performance under external disturbances, and enables effective infrastructure scaling. The findings form a scientific foundation for designing adaptive, robust, and self-learning next-generation digital ecosystems capable of intelligent processing of large data streams in real time

Recent advances and widespread usage of online web services and social media platforms, coupled with ubiquitous low cost devices, mobile technologies, and increasing capacity of lower cost storage, has led to a proliferation of Big data, ranging from, news, e-commerce clickstreams, and online business transactions to continuous event logs and social media expressions. These large amounts of online data, often referred to as data streams, because they get generated at extremely high throughputs or velocity, can make conventional and classical data analytics methodologies obsolete. For these reasons, the issues of management and analysis of data streams have been researched extensively in recent years. The special case of social media Big Data brings additional challenges, particularly because of the unstructured nature of the data, specifically free text. One classical approach to mine text data has been Topic Modeling. Topic Models are statistical models that can be used for discovering the abstract ``topics'' that may occur in a corpus of documents. Topic models have emerged as a powerful technique in machine learning and data science, providing a great balance between simplicity and complexity. They also provide sophisticated insight without the need for real natural language understanding. However they have not been designed to cope with the type of text data that is abundant on social media platforms, but rather for traditional medium size corpora consisting of longer documents, adhering to a specific language and typically spanning a stable set of topics. Unlike traditional document corpora, social media messages tend to be very short, sparse, noisy, and do not adhere to a standard vocabulary, linguistic patterns, or stable topic distributions. They are also generated at high velocity that impose high demands on topic modeling; and their evolving or dynamic nature, makes any set of results from topic modeling quickly become stale in the face of changes in the textual content and topics discussed within social media streams. In this dissertation, we propose an integrated topic modeling framework built on top of an existing stream-clustering framework called Stream-Dashboard, which can extract, isolate, and track topics over any given time period. In this new framework, Stream Dashboard first clusters the data stream points into homogeneous groups. Then data from each group is ushered to the topic modeling framework which extracts finer topics from the group. The proposed framework tracks the evolution of the clusters over time to detect milestones corresponding to changes in topic evolution,

Real-time monitoring systems are important for industry since they allow for avoiding unplanned system stops and keeping system availability high. The technical requirements for such systems include being both scalable and online, as the amount of generated data is increasing with time. Therefore, monitoring systems must integrate tools that can manage and analyze the data streams. The data stream management system is a stream processing tool that has the ability to manage and support operations on data streams in real-time. Several researchers have proposed and tested real-time monitoring systems which have the ability to search big data streams. In this paper, the research works that discuss the analysis of online data streams for fault detection in industry are reviewed. Based on the literature analysis, the industrial needs and challenges of monitoring big data streams are presented. Furthermore, feasible suggestions for improving the real-time monitoring system are proposed.

Cloud computing and big data: Technologies and applications

The confluence of distributed stream processing and Artificial Intelligence (AI) / Machine Learning (ML) presents significant opportunities for real-time intelligent applications.This research plan outlines an investigation into "Real-time Distributed AI on Streaming Data," focusing on challenges and advancements pertinent to the software engineering community, with a forward-looking perspective towards 2025.It aims to synthesize existing open-access literature to provide a comprehensive overview of the current state, identify key software engineering hurdles, and explore cutting-edge research directions.The emphasis is on systems like Apache Flink, Apache Spark Streaming, and Kafka Streams that process massive, continuous data flows, embedding AI/ML inference and online learning capabilities directly within these pipelines with stringent lowlatency requirements.

The real time analysis of massive data streams is of utmost importance in data intensive applications that need to detect as fast as possible and as efficiently as possible (in terms of computation and memory space) any correlation between its inputs or any deviance from some expected nominal behavior. The IoT infrastructure can be used for monitoring any events or changes in structural conditions that can compromise safety and increase risk. It is thus a recurrent and crucial issue to determine whether huge data streams, received at monitored devices, are correlated or not as it may reveal the presence of attacks. We propose a metric, called codeviation, that allows to evaluate the correlation between distributed massive streams. This metric is inspired from classical metric in statistics and probability theory, and as such enables to understand how observed quantities change together, and in which proportion. We then propose to estimate the codeviation in the data stream model. In this model, functions are estimated on a huge sequence of data items, in an online fashion, and with a very small amount of memory with respect to both the size of the input stream and the values domain from which data items are drawn. We then generalize our approach by presenting a new metric, the Sketch-\(\star \) metric, which allows us to define a distance between updatable summaries of large data streams. An important feature of the Sketch-\(\star \) metric is that, given a measure on the entire initial data streams, the Sketch-\(\star \) metric preserves the axioms of the latter measure on the sketch. We finally present results obtained during extensive experiments conducted on both synthetic traces and real data sets allowing us to validate the robustness and accuracy of our metrics.

Recent advances in storage and processing have provided the possibility of automatic gathering of information, which in turn leads to fast and continuous flows of data. The data which are produced and stored in this way are called data streams. Data streams are produced in large size, and much dynamism and have some unique properties which make them applicable to model many real data mining applications. The main challenge of streaming data is the occurrence of concept drift. In addition, regarding the costs of labeling of instances, it is often assumed that only a small fraction of instances are labeled. In this paper, we propose an ensemble algorithm to classify instances of non-stationary data streams in a semi-supervised environment. Furthermore, this method is intended to recognize recurring concept drifts of data streams. In the proposed algorithm, a pool of classifiers is maintained by the algorithm with each classifier being representative of one single concept. At first, a batch of instances is classified by the algorithm. Thereafter, some of these instances are labeled and this partially labeled batch is used to update the classifiers in the pool. This process repeats for consecutive batches of the streams. The main advantage of the algorithm is that it uses unlabeled instances as well as labeled ones in the learning task. Experimental results show the effectiveness of the proposed algorithm over the state-of-the-art methods, in different aspects.

Over the last decade, several interconnected disruptions have happened in the large scale distributed and parallel computing landscape. The volume of data currently produced by various activities of the society has never been so big and is generated at an increasing speed. Data that is received in real-time can become way too valuable at the time it arrives and supports valuable decision making. Systems for managing data streams is not a recently developed concept but its becoming more important due to the multiplication of data stream sources in the context of IoT. This paper refers to the unique processing challenges posed by the nature of streams, and the related mechanisms used to face them in the big data era. Several cloud systems emerged to enable distributed processing of streams of big data. Distributed stream management systems (DSMS) along with their strengths and limitations are presented and compared. Computations in these systems demand elaborate orchestration over a collection of machines. Consequently, a classification and literature review on these systems' scheduling techniques and their enhancements is also provided.