S-Store: Streaming Meets Transaction Processing

Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees

A systematic literature review of skyline query processing over data stream

Recently, skyline query processing over data stream has gained a lot of attention especially from the database community owing to its own unique challenges. Skyline queries aims at pruning a search space of a potential large multi-dimensional set of objects by keeping only those objects that are not worse than any other. Although an abundance of skyline query processing techniques have been proposed, there is a lack of a Systematic Literature Review (SLR) on current research works pertinent to skyline query processing over data stream. In regard to this, this paper provides a comparative study on the state-of-the-art approaches over the period between 2000 and 2022 with the main aim to help readers understand the key issues which are essential to consider in relation to processing skyline queries over streaming data. Seven digital databases were reviewed in accordance with the Preferred Reporting Items for Systematic Reviews (PRISMA) procedures. After applying both the inclusion and exclusion criteria, 23 primary papers were further examined. The results show that the identified skyline approaches are driven by the need to expedite the skyline query processing mainly due to the fact that data streams are time varying (time sensitive), continuous, real time, volatile, and unrepeatable. Although, these skyline approaches are tailored made for data stream with a common aim, their solutions vary to suit with the various aspects being considered, which include the type of skyline query, type of streaming data, type of sliding window, query processing technique, indexing technique as well as the data stream environment employed. In this paper, a comprehensive taxonomy is developed along with the key aspects of each reported approach, while several open issues and challenges related to the topic being reviewed are highlighted as recommendation for future research direction

Parallel paradigms for Data Stream Processing

The aim of this thesis is to address Data Stream Processing issues from the point of view of High Performance Computing. In particular our work focused on the definition of parallel paradigms for DaSP problems. An implementation of a parallel scheme for the solution of the stream join problem is given along with the tests performed and their analysis

Parallel paradigms for Data Stream Processing

The aim of this thesis is to address Data Stream Processing issues from the point of view of High Performance Computing. In particular our work focused on the definition of parallel paradigms for DaSP problems. An implementation of a parallel scheme for the solution of the stream join problem is given along with the tests performed and their analysis

Parallel Patterns for Adaptive Data Stream Processing

In recent years our ability to produce information has been growing steadily, driven by an ever increasing computing power, communication rates, hardware and software sensors diffusion. This data is often available in the form of continuous streams and the ability to gather and analyze it to extract insights and detect patterns is a valuable opportunity for many businesses and scientific applications. The topic of Data Stream Processing (DaSP) is a recent and highly active research area dealing with the processing of this streaming data. The development of DaSP applications poses several challenges, from efficient algorithms for the computation to programming and runtime systems to support their execution. In this thesis two main problems will be tackled: * need for high performance: high throughput and low latency are critical requirements for DaSP problems. Applications necessitate taking advantage of parallel hardware and distributed systems, such as multi/manycores or cluster of multicores, in an effective way; * dynamicity: due to their long running nature (24hr/7d), DaSP applications are affected by highly variable arrival rates and changes in their workload characteristics. Adaptivity is a fundamental feature in this context: applications must be able to autonomously scale the used resources to accommodate dynamic requirements and workload while maintaining the desired Quality of Service (QoS) in a cost-effective manner. In the current approaches to the development of DaSP applications are still missing efficient exploitation of intra-operator parallelism as well as adaptations strategies with well known properties of stability, QoS assurance and cost awareness. These are the gaps that this research work tries to fill, resorting to well know approaches such as Structured Parallel Programming and Control Theoretic models. The dissertation runs along these two directions. The first part deals with intra-operator parallelism. A DaSP application can be naturally expressed as a set of operators (i.e. intermediate computations) that cooperate to reach a common goal. If QoS requirements are not met by the current implementation, bottleneck operators must be internally parallelized. We will study recurrent computations in window based stateful operators and propose patterns for their parallel implementation. Windowed operators are the most representative class of stateful data stream operators. Here computations are applied on the most recent received data. Windows are dynamic data structures: they evolve over time in terms of content and, possibly, size. Therefore, with respect to traditional patterns, the DaSP domain requires proper specializations and enhanced features concerning data distribution and management policies for different windowing methods. A structured approach to the problem will reduce the effort and complexity of parallel programming. In addition, it simplifies the reasoning about the performance properties of a parallel solution (e.g. throughput and latency). The proposed patterns exhibit different properties in terms of applicability and profitability that will be discussed and experimentally evaluated. The second part of the thesis is devoted to the proposal and study of predictive strategies and reconfiguration mechanisms for autonomic DaSP operators. Reconfiguration activities can be implemented in a transparent way to the application programmer thanks to the exploitation of parallel paradigms with well known structures. Furthermore, adaptation strategies may take advantage of the QoS predictability of the used parallel solution. Autonomous operators will be driven by means of a Model Predictive Control approach, with the intent of giving QoS assurances in terms of throughput or latency in a resource-aware manner. An experimental section will show the effectiveness of the proposed approach in terms of execution costs reduction as well as the stability degree of a system reconfiguration. The experiments will target shared and distributed memory architectures

A Platform for Scalable Low-Latency Analytics using MapReduce

Today, the ability to process big data has become crucial to the information needs of many enterprise businesses, scientific applications, and governments. Recently, there have been increasing needs of processing data that is not only big but also fast . Here fast data refers to high-speed real-time and near real-time data streams, such as Twitter feeds, search query streams, click streams, impressions, and system logs. To handle both historical data and real-time data, many companies have to maintain multiple systems. However, recent real-world case studies show that maintaining multiple systems cause not only code duplication, but also intensive manual work to partition the analytics workloads and determine which data is processed by which system. These issues point to the need for a general, unified data processing framework to support analytical queries with different latency requirements. This thesis takes a further step towards building a general, unified system for big and fast data analytics. In order to build such a system, I propose to build on existing solutions on data parallelism and extend them with two new features: incremental processing and stream processing with latency constraints. This thesis starts with Hadoop, the most popular open-source MapReduce implementation, which provides proven scalability based on data parallelism. I answer the following questions: (1) Is Hadoop able to support incremental processing? (2) What are the necessary architecture changes in order to support incremental processing? (3) What are the additional design features needed to support stream processing with latency constraints? The thesis includes three parts that answer each of the questions. The first part of the thesis validates whether the existing MapReduce implementations can support incremental processing. Incremental processing means that computation is performed as soon as the relevant data becomes available. My extensive benchmark study of Hadoop-based MapReduce systems shows that the widely-used sort-merge implementation for partitioning and parallel processing poses a fundamental barrier to incremental computation. I further propose a cost model, and optimize the Hadoop system configuration based on the model. The benchmark results over the optimized system verify that the barrier to incremental computation is intrinsic, and cannot be removed by tuning system parameters. In the second part of the thesis, I employ various purely hash-based techniques to enable fast in-memory incremental processing in MapReduce, and frequent key based techniques to extend such processing to workloads that require memory more than available. I evaluate my Hadoop-based prototype equipped with all proposed techniques. The results show that the hash techniques allow the reduce progress to keep up with the map progress with up to 3 orders of magnitude reduction of internal disk spills, and enable results to be returned early. The third part of the thesis aims to support stream processing with latency constraints based on the incremental processing platform resulted from the second part. I perform a benchmark study to understand the sources of latency. I then propose a number of necessary architecture changes to support stream processing, and augment the platform with new latency-aware model-driven resource planning and latency-aware runtime scheduling techniques to meet user-specified latency constraints while maximizing throughput. Experiments using real-world workloads show that the techniques reduce the latency from tens or hundreds of seconds to sub-second, with 2x-5x increase in throughput. The new platform offers 1-2 orders of magnitude improvements over Storm, a commercial-grade distributed stream system, and Spark Streaming, a state-of-the-art academic prototype, when considering both latency and throughput

Extending Complex Event Processing for Advanced Applications

Recently numerous emerging applications, ranging from on-line financial transactions, RFID based supply chain management, traffic monitoring to real-time object monitoring, generate high-volume event streams. To meet the needs of processing event data streams in real-time, Complex Event Processing technology (CEP) has been developed with the focus on detecting occurrences of particular composite patterns of events. By analyzing and constructing several real-world CEP applications, we found that CEP needs to be extended with advanced services beyond detecting pattern queries. We summarize these emerging needs in three orthogonal directions. First, for applications which require access to both streaming and stored data, we need to provide a clear semantics and efficient schedulers in the face of concurrent access and failures. Second, when a CEP system is deployed in a sensitive environment such as health care, we wish to mitigate possible privacy leaks. Third, when input events do not carry the identification of the object being monitored, we need to infer the probabilistic identification of events before feed them to a CEP engine. Therefore this dissertation discusses the construction of a framework for extending CEP to support these critical services. First, existing CEP technology is limited in its capability of reacting to opportunities and risks detected by pattern queries. We propose to tackle this unsolved problem by embedding active rule support within the CEP engine. The main challenge is to handle interactions between queries and reactions to queries in the high-volume stream execution. We hence introduce a novel stream-oriented transactional model along with a family of stream transaction scheduling algorithms that ensure the correctness of concurrent stream execution. And then we demonstrate the proposed technology by applying it to a real-world healthcare system and evaluate the stream transaction scheduling algorithms extensively using real-world workload. Second, we are the first to study the privacy implications of CEP systems. Specifically we consider how to suppress events on a stream to reduce the disclosure of sensitive patterns, while ensuring that nonsensitive patterns continue to be reported by the CEP engine. We formally define the problem of utility-maximizing event suppression for privacy preservation. We then design a suite of real-time solutions that eliminate private pattern matches while maximizing the overall utility. Our first solution optimally solves the problem at the event-type level. The second solution, at event-instance level, further optimizes the event-type level solution by exploiting runtime event distributions using advanced pattern match cardinality estimation techniques. Our experimental evaluation over both real-world and synthetic event streams shows that our algorithms are effective in maximizing utility yet still efficient enough to offer near real time system responsiveness. Third, we observe that in many real-world object monitoring applications where the CEP technology is adopted, not all sensed events carry the identification of the object whose action they report on, so called €œnon-ID-ed€� events. Such non-ID-ed events prevent us from performing object-based analytics, such as tracking, alerting and pattern matching. We propose a probabilistic inference framework to tackle this problem by inferring the missing object identification associated with an event. Specifically, as a foundation we design a time-varying graphic model to capture correspondences between sensed events and objects. Upon this model, we elaborate how to adapt the state-of-the-art Forward-backward inference algorithm to continuously infer probabilistic identifications for non-ID-ed events. More important, we propose a suite of strategies for optimizing the performance of inference. Our experimental results, using large-volume streams of a real-world health care application, demonstrate the accuracy, efficiency, and scalability of the proposed technology

Engineering Crowdsourced Stream Processing Systems

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort

Exploring time related issues in data stream processing

Ph.DDOCTOR OF PHILOSOPH

State Management for Efficient Event Pattern Detection

Event Stream Processing (ESP) Systeme überwachen kontinuierliche Datenströme, um benutzerdefinierte Queries auszuwerten. Die Herausforderung besteht darin, dass die Queryverarbeitung zustandsbehaftet ist und die Anzahl von Teilübereinstimmungen mit der Größe der verarbeiteten Events exponentiell anwächst. Die Dynamik von Streams und die Notwendigkeit, entfernte Daten zu integrieren, erschweren die Zustandsverwaltung. Erstens liefern heterogene Eventquellen Streams mit unvorhersehbaren Eingaberaten und Queryselektivitäten. Während Spitzenzeiten ist eine erschöpfende Verarbeitung unmöglich, und die Systeme müssen auf eine Best-Effort-Verarbeitung zurückgreifen. Zweitens erfordern Queries möglicherweise externe Daten, um ein bestimmtes Event für eine Query auszuwählen. Solche Abhängigkeiten sind problematisch: Das Abrufen der Daten unterbricht die Stream-Verarbeitung. Ohne eine Eventauswahl auf Grundlage externer Daten wird das Wachstum von Teilübereinstimmungen verstärkt. In dieser Dissertation stelle ich Strategien für optimiertes Zustandsmanagement von ESP Systemen vor. Zuerst ermögliche ich eine Best-Effort-Verarbeitung mittels Load Shedding. Dabei werden sowohl Eingabeeevents als auch Teilübereinstimmungen systematisch verworfen, um eine Latenzschwelle mit minimalem Qualitätsverlust zu garantieren. Zweitens integriere ich externe Daten, indem ich das Abrufen dieser von der Verwendung in der Queryverarbeitung entkoppele. Mit einem effizienten Caching-Mechanismus vermeide ich Unterbrechungen durch Übertragungslatenzen. Dazu werden externe Daten basierend auf ihrer erwarteten Verwendung vorab abgerufen und mittels Lazy Evaluation bei der Eventauswahl berücksichtigt. Dabei wird ein Kostenmodell verwendet, um zu bestimmen, wann welche externen Daten abgerufen und wie lange sie im Cache aufbewahrt werden sollen. Ich habe die Effektivität und Effizienz der vorgeschlagenen Strategien anhand von synthetischen und realen Daten ausgewertet und unter Beweis gestellt.Event stream processing systems continuously evaluate queries over event streams to detect user-specified patterns with low latency. However, the challenge is that query processing is stateful and it maintains partial matches that grow exponentially in the size of processed events. State management is complicated by the dynamicity of streams and the need to integrate remote data. First, heterogeneous event sources yield dynamic streams with unpredictable input rates, data distributions, and query selectivities. During peak times, exhaustive processing is unreasonable, and systems shall resort to best-effort processing. Second, queries may require remote data to select a specific event for a pattern. Such dependencies are problematic: Fetching the remote data interrupts the stream processing. Yet, without event selection based on remote data, the growth of partial matches is amplified. In this dissertation, I present strategies for optimised state management in event pattern detection. First, I enable best-effort processing with load shedding that discards both input events and partial matches. I carefully select the shedding elements to satisfy a latency bound while striving for a minimal loss in result quality. Second, to efficiently integrate remote data, I decouple the fetching of remote data from its use in query evaluation by a caching mechanism. To this end, I hide the transmission latency by prefetching remote data based on anticipated use and by lazy evaluation that postpones the event selection based on remote data to avoid interruptions. A cost model is used to determine when to fetch which remote data items and how long to keep them in the cache. I evaluated the above techniques with queries over synthetic and real-world data. I show that the load shedding technique significantly improves the recall of pattern detection over baseline approaches, while the technique for remote data integration significantly reduces the pattern detection latency