is. A stream S is a possibly infinite bag of elements (x, t) where x is a tuple belonging to the schema S and t ∈ T is the timestamp of the elements [3]. Data stream refers to an unbounded and ordered sequence of instances of data arriving over time [4]. Data stream can be formally defined as an infinite sequence of tuples S = (x1, ti), (x2, t2),…, (xn, tn),… where xi is a tuple and ti is a timestamp [5]. Streaming data can be defined as frequently changing, and potentially infinite data flow generated from disparate sources [6]. Formally, streaming data Table 1 Streaming data versus static data [9, 10]
| Dimension | Streaming data | Static data |
|---|---|---|
| Hardware | Typical single constrained measure of memory | Multiple CPUs |
| Input | Data streams or updates | Data chunks |
| Time | A few moments or even milliseconds | Much longer |
| Data size | Infinite or unknown in advance | Known and finite |
| Processing | A single or few pass over data | Processes in multiple rounds |
| Storage | Not store or store a significant portion in memory | Store |
| Applications | Web mining, traffic monitoring, sensor networks | Widely adopted in many domains |
Source: Tozi, C. (2017). Dummy's guide to batch vs streaming. Retrieved from Trillium Software, Retrieved from http://blog.syncs ort.com/2017/07/bigdata/; Kolajo, T., Daramola, O. & Adebiyi, A. (2019). Big data stream analysis: A systematic literature review, Journal of Big Data 6(47).
The ocean of streaming data continuously generated through various mediums such as sensors, ATM transactions, and the web is tremendously increasing, and recognizing patterns in these mediums is equally challenging [8]. Most methods used for data stream mining are adapted from techniques designed for a finite or static dataset. Data stream mining imposes a high number of constraints on canonical algorithms. To quickly appreciate these constraints, the differences between static and streaming scenarios are presented in Table 1.
In the big data era, data stream mining serves as one of the vital fields. Since streaming data is continuous, unlimited, and with nonuniform distribution, there is the need for efficient data structures and algorithms to mine patterns from this high volume, high traffic, often imbalanced data stream that is also plagued with concept drift [11].
This chapter intends to broaden the existing knowledge in the domain of data science, streaming data, and data streams. To do this, relevant themes including data stream mining issues, streaming data tools and technologies, streaming data pre‐processing, streaming data algorithms, strategies for processing data streams, best practices for managing data streams, and suggestions for the way forward are discussed in this chapter. The structure of the rest of this chapter is as follows. Section 2 presents a brief background on data stream computing; Section 3 discusses issues in data stream mining, tools, and technologies for data streaming are presented in Sections 4 while streaming data pre‐processing is discussed in Section 5. Sections 6 and 7 present streaming data algorithms and data stream processing strategies, respectively. This is followed by a discussion on best practices for managing data streams in Section 8, while the conclusion and some ideas on the way forward are presented in Section 9.
2 Data Stream Computing
Data stream computing alludes to the real‐time processing of vast measures of data produced at high speed from numerous sources, with different schemas, and different temporal resolutions [12]. It is another required worldview given the new wellsprings of data‐generation situations, which incorporates the cell phones, ubiquity of location services, and sensor universality [13].
The principal presumption of stream computing is that the likelihood estimation of data lies in its newness. Thus, the analysis of data is done the moment they arrive in a stream instead of what obtains in batch processing where data are first stored before they are analyzed. This is a serious requirement for suitable platforms for scalable computing with parallel architectures [14]. With stream computing, it is feasible for organizations to analyze and respond to speedily changing data in real‐time [15]. Integrating streaming data into the decision‐making process brings about a programming concept called stream computing. Stream processing solutions ought to have the option to deal with the high volume of data from different sources in real‐time by giving due consideration to accessibility, versatility, and adaptation to noncritical failure. Datastream analysis includes the ingestion of data as a boundless tuple, analysis, and creation of significant outcomes in a stream [16].
In a stream processor, the representation of an application is done with the data flow graph, which is comprised of operations and interconnected streams. A stream processing workflow consists of programs. Formally, a composition
