A Survey of Distributed Data Stream Processing Frameworks <s> 5) OUTPUT LAYER <s> Information visualization (InfoVis), the study of transforming data, information, and knowledge into interactive visual representations, is very important to users because it provides mental models of information. The boom in big data analytics has triggered broad use of InfoVis in a variety of domains, ranging from finance to sports to politics. In this paper, we present a comprehensive survey and key insights into this fast-rising area. The research on InfoVis is organized into a taxonomy that contains four main categories, namely empirical methodologies, user interactions, visualization frameworks, and applications, which are each described in terms of their major goals, fundamental principles, recent trends, and state-of-the-art approaches. At the conclusion of this survey, we identify existing technical challenges and propose directions for future research. <s> BIB001 </s> A Survey of Distributed Data Stream Processing Frameworks <s> 5) OUTPUT LAYER <s> Under several emerging application scenarios, such as in smart cities, operational monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous data streams must be processed under very short delays. Several solutions, including multiple software engines, have been developed for processing unbounded data streams in a scalable and efficient manner. More recently, architecture has been proposed to use edge computing for data stream processing. This paper surveys state of the art on stream processing engines and mechanisms for exploiting resource elasticity features of cloud computing in stream processing. Resource elasticity allows for an application or service to scale out/in according to fluctuating demands. Although such features have been extensively investigated for enterprise applications, stream processing poses challenges on achieving elastic systems that can make efficient resource management decisions based on current load. Elasticity becomes even more challenging in highly distributed environments comprising edge and cloud computing resources. This work examines some of these challenges and discusses solutions proposed in the literature to address them. <s> BIB002 </s> A Survey of Distributed Data Stream Processing Frameworks <s> 5) OUTPUT LAYER <s> Data Stream Processing (DSP) has emerged as a key enabler to develop pervasive services that require to process data in a near real-time fashion. DSP applications keep up with the high volume of produced data by scaling their execution on multiple computing nodes, so as to process the incoming data flow in parallel. Workloads variability requires to elastically adapt the application parallelism at run-time in order to avoid over-provisioning. Elasticity policies for DSP have been widely investigated, but mostly under the simplifying assumption of homogeneous infrastructures. The resulting solutions do not capture the richness and inherent complexity of modern infrastructures, where heterogeneous computing resources are available on-demand. In this paper, we formulate the problem of controlling elasticity on heterogeneous resources as a Markov Decision Process (MDP). The resulting MDP is not easily solved by traditional techniques due to state space explosion, and thus we show how linear Function Approximation and Tile Coding can be used to efficiently compute elasticity policies at run-time. In order to deal with parameters uncertainty, we integrate the proposed approach with Reinforcement Learning algorithms. Our numerical evaluation shows the efficacy of the presented solutions compared to standard methods in terms of accuracy and convergence speed. <s> BIB003
The results from data stream processing pipelines can be directed to an application, another workflow, a visualization tool, or an alert or monitoring dashboard BIB002 . At the same time, data and extracted knowledge can be passed on to the temporary in-memory or permanent storage for subsequent analysis and query. Different data types and structures require specialized tool sets to visualize. These tools were categorized by Liu et al. BIB001 into four groups: (i) graph visualization which includes static graph visualization (matrix representation and node-link diagrams) using tools such as TreeNetViz, and dynamic graph visualization (mental maps, animation techniques) using tools such as StoryFlow, (ii) text visualization which includes static text visualization (feature-based and topic-based) using tools such as Word Clouds or Phrase Nets, and dynamic visualization using tools such as SparkClouds, TextFlow and EventRiver, (iii) map visualization for geographic data exploration using tools such as BirdVis, and (iv) multivariate data visualization for generic data types using Multidimensional Scaling (MDS) projection tools. Visualization tools are often utilized in decision support systems (DSS) for both streaming and non-streaming data applications  , BIB001 ,  . In this paper, we mainly focus on the comparative study of DSPEs which lie at the core of DSPSs and enable definition and execution of stream processing pipelines BIB003 . Key features of the distributed DSPEs are described in the next section.