Parallel Continuous Preference Queries over Out-of-Order and Bursty Data Streams

Techniques to handle traffic bursts and out-of-order arrivals are of paramount importance to provide real-time sensor data analytics in domains like traffic surveillance, transportation management, healthcare and security applications. In these systems the amount of raw data coming from sensors must be analyzed by continuous queries that extract value-added information used to make informed decisions in real-time. To perform this task with timing constraints, parallelism must be exploited in the query execution in order to enable the real-time processing on parallel architectures. In this paper we focus on continuous preference queries, a representative class of continuous queries for decision making, and we propose a parallel query model targeting the efficient processing over out-of-order and bursty data streams. We study how to integrate punctuation mechanisms in order to enable out-of-order processing. Then, we present advanced scheduling strategies targeting scenarios with different burstiness levels, parameterized using the index of dispersion quantity. Extensive experiments have been performed using synthetic datasets and real-world data streams obtained from an existing real-time locating system. The experimental evaluation demonstrates the efficiency of our parallel solution and its effectiveness in handling the out-of-orderness degrees and burstiness levels of real-world applications

Saber: window-based hybrid stream processing for heterogeneous architectures

Modern servers have become heterogeneous, often combining multicore CPUs with many-core GPGPUs. Such heterogeneous architectures have the potential to improve the performance of data-intensive stream processing applications, but they are not supported by current relational stream processing engines. For an engine to exploit a heterogeneous architecture, it must execute streaming SQL queries with sufficient data-parallelism to fully utilise all available heterogeneous processors, and decide how to use each in the most effective way. It must do this while respecting the semantics of streaming SQL queries, in particular with regard to window handling. We describe SABER, a hybrid high-performance relational stream processing engine for CPUs and GPGPUs. SABER executes windowbased streaming SQL queries in a data-parallel fashion using all available CPU and GPGPU cores. Instead of statically assigning query operators to heterogeneous processors, SABER employs a new adaptive heterogeneous lookahead scheduling strategy, which increases the share of queries executing on the processor that yields the highest performance. To hide data movement costs, SABER pipelines the transfer of stream data between different memory types and the CPU/GPGPU. Our experimental comparison against state-ofthe-art engines shows that SABER increases processing throughput while maintaining low latency for a wide range of streaming SQL queries with small and large windows sizes

An efficient time optimized scheme for progressive analytics in big data

Big data analytics is the key research subject for future data driven decision making applications. Due to the large amount of data, progressive analytics could provide an efficient way for querying big data clusters. Each cluster contains only a piece of the examined data. Continuous queries over these data sources require intelligent mechanisms to result the final outcome (query response) in the minimum time with the maximum performance. A Query Controller (QC) is responsible to manage continuous/sequential queries and return the final outcome to users or applications. In this paper, we propose a mechanism that can be adopted by the QC. The proposed mechanism is capable of managing partial results retrieved by a number of processors each one responsible for each cluster. Each processor executes a query over a specific cluster of data. Our mechanism adopts two sequential decision making models for handling the incoming partial results. The first model is based on a finite horizon time-optimized model and the second one is based on an infinite horizon optimally scheduled model. We provide mathematical formulations for solving the discussed problem and present simulation results. Through a large number of experiments, we reveal the advantages of the proposed models and give numerical results comparing them with a deterministic model. These results indicate that the proposed models can efficiently reduce the required time for returning the final outcome to the user/application while keeping the quality of the aggregated result at high levels

MTopS: Multi-Query Optimization for Continuous Top-K Query Workloads

A continuous top-k query retrieves the k most preferred objects from a data stream according to a given preference function. These queries are important for a broad spectrum of applications from web-based advertising, network traffic monitoring, to financial analysis. Given the nature of such applications, a data stream may be subjected at any given time to multiple top-k queries with varying parameter settings requested simultaneously by different users. This workload of simultaneous top-k queries must be executed efficiently to assure real time responsiveness. However, existing methods in the literature focus on optimizing single top-k query processing, thus would handle each query independently. They are thus not suitable for handling large numbers of such simultaneous top-k queries due to their unsustainable resource demands. In this thesis, we present a comprehensive framework, called MTopS for Multiple Top-K Optimized Processing System. MTopS achieves resource sharing at the query level by analyzing parameter settings of all queries in the workload, including window-specific parameters and top-k parameters. We further optimize the shared processing by identifying the minimal object set from the data stream that is both necessary and sufficient for top-k monitoring of all queries in the workload. Within this framework, we design the MTopBand algorithm that maintains the up-to-date top-k result set in the size of O (k), where k is the required top-k result set, eliminating the need for any recomputation. To overcome the overhead caused by MTopBand to maintain replicas of the top-k result set across sliding windows, we optimize this algorithm further by integrating these views into one integrated structure, called MTopList. Our associated top-k maintenance algorithm, also called MTopList algorithm, is able to maintain this linear integrated structure, thus able to efficiently answer all queries in the workload. MTopList is shown to be memory optimal because it maintains only the distinct objects that are part of top-k results of at least one query. Our experimental study, using real data streams from domains of stock trades and moving object monitoring, demonstrates that both the efficiency and scalability in the query workload of our proposed technique is superior to the state-of-the-art solutions

Continuous Nearest Neighbor Queries over Sliding Windows

Abstract—This paper studies continuous monitoring of nearest neighbor (NN) queries over sliding window streams. According to this model, data points continuously stream in the system, and they are considered valid only while they belong to a sliding window that contains 1) the W most recent arrivals (count-based) or 2) the arrivals within a fixed interval W covering the most recent time stamps (time-based). The task of the query processor is to constantly maintain the result of long-running NN queries among the valid data. We present two processing techniques that apply to both count-based and time-based windows. The first one adapts conceptual partitioning, the best existing method for continuous NN monitoring over update streams, to the sliding window model. The second technique reduces the problem to skyline maintenance in the distance-time space and precomputes the future changes in the NN set. We analyze the performance of both algorithms and extend them to variations of NN search. Finally, we compare their efficiency through a comprehensive experimental evaluation. The skyline-based algorithm achieves lower CPU cost, at the expense of slightly larger space overhead. Index Terms—Location-dependent and sensitive, spatial databases, query processing, nearest neighbors, data streams, sliding windows.

Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

Reinforcement machine learning for predictive analytics in smart cities

The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal smart devices as well as the Internet of Things (IoT) paradigm lead to a vast infrastructure that covers all the aspects of activities in modern societies. In the most of the cases, the critical issue for public authorities (usually, local, like municipalities) is the efficient management of data towards the support of novel services. The reason is that analytics provided on top of the collected data could help in the delivery of new applications that will facilitate citizens’ lives. However, the provision of analytics demands intelligent techniques for the underlying data management. The most known technique is the separation of huge volumes of data into a number of parts and their parallel management to limit the required time for the delivery of analytics. Afterwards, analytics requests in the form of queries could be realized and derive the necessary knowledge for supporting intelligent applications. In this paper, we define the concept of a Query Controller ( QC ) that receives queries for analytics and assigns each of them to a processor placed in front of each data partition. We discuss an intelligent process for query assignments that adopts Machine Learning (ML). We adopt two learning schemes, i.e., Reinforcement Learning (RL) and clustering. We report on the comparison of the two schemes and elaborate on their combination. Our aim is to provide an efficient framework to support the decision making of the QC that should swiftly select the appropriate processor for each query. We provide mathematical formulations for the discussed problem and present simulation results. Through a comprehensive experimental evaluation, we reveal the advantages of the proposed models and describe the outcomes results while comparing them with a deterministic framework

Continuously Providing Approximate Results under Limited Resources: Load Shedding and Spilling in XML Streams

Because of the high volume and unpredictable arrival rates, stream processing systems may not always be able to keep up with the input data streams, resulting in buffer overflow and uncontrolled loss of data. To continuously supply online results, two alternate solutions to tackle this problem of unpredictable failures of such overloaded systems can be identified. One technique, called load shedding, drops some fractions of data from the input stream to reduce the memory and CPU requirements of the workload. However, dropping some portions of the input data means that the accuracy of the output is reduced since some data is lost. To produce eventually complete results, the second technique, called data spilling, pushes some fractions of data to persistent storage temporarily when the processing speed cannot keep up with the arrival rate. The processing of the disk resident data is then postponed until a later time when system resources become available. This dissertation explores these load reduction technologies in the context of XML stream systems. Load shedding in the specific context of XML streams poses several unique opportunities and challenges. Since XML data is hierarchical, subelements, extracted from different positions of the XML tree structure, may vary in their importance. Further, dropping different subelements may vary in their savings of storage and computation. Hence, unlike prior work in the literature that drops data completely or not at all, in this dissertation we introduce the notion of structure-oriented load shedding, meaning selectively some XML subelements are shed from the possibly complex XML objects in the XML stream. First we develop a preference model that enables users to specify the relative importance of preserving different subelements within the XML result structure. This transforms shedding into the problem of rewriting the user query into shed queries that return approximate answers with their utility as measured by the user preference model. Our optimizer finds the appropriate shed queries to maximize the output utility driven by our structure-based preference model under the limitation of available computation resources. The experimental results demonstrate that our proposed XML-specific shedding solution consistently achieves higher utility results compared to the existing relational shedding techniques. Second, we introduces structure-based spilling, a spilling technique customized for XML streams by considering the spilling of partial substructures of possibly complex XML elements. Several new challenges caused by structure-based spilling are addressed. When a path is spilled, multiple other paths may be affected. We categorize varying types of spilling side effects on the query caused by spilling. How to execute the reduced query to produce the correct runtime output is also studied. Three optimization strategies are developed to select the reduced query that maximizes the output quality. We also examine the clean-up stage to guarantee that an entire result set is eventually generated by producing supplementary results to complement the partial results output earlier. The experimental study demonstrates that our proposed solutions consistently achieve higher quality results compared to the state-of-the-art techniques. Third, we design an integrated framework that combines both shedding and spilling policies into one comprehensive methodology. Decisions on the choice of whether to shed or spill data may be affected by the application needs and data arrival patterns. For some input data, it may be worth to flush it to disk if a delayed output of its result will be important, while other data would best directly dropped from the system given that a delayed delivery of these results would no longer be meaningful to the application. Therefore we need sophisticated technologies capable of deploying both shedding and spilling techniques within one integrated strategy with the ability to deliver the most appropriate decision customers need for each specific circumstance. We propose a novel flexible framework for structure-based shed and spill approaches, applicable in any XML stream system. We propose a solution space that represents all the shed and spill candidates. An age-based quality model is proposed for evaluating the output quality for different reduced query and supplementary query pairs. We also propose a family of four optimization strategies, OptF, OptSmart, HiX and Fex. OptF and OptSmart are both guaranteed to identify an optimal solution of reduced and supplementary query pair, with OptSmart exhibiting significantly less overhead than OptF. HiX and Fex use heuristic-based approaches that are much more efficient than OptF and OptSmart

Augmenting conversations through context-aware multimedia retrieval based on speech recognition

Future’s environments will be sensitive and responsive to the presence of people to support them carrying out their everyday life activities, tasks and rituals, in an easy and natural way. Such interactive spaces will use the information and communication technologies to bring the computation into the physical world, in order to enhance ordinary activities of their users. This paper describes a speech-based spoken multimedia retrieval system that can be used to present relevant video-podcast (vodcast) footage, in response to spontaneous speech and conversations during daily life activities. The proposed system allows users to search the spoken content of multimedia files rather than their associated meta-information and let them navigate to the right portion where queried words are spoken by facilitating within-medium searches of multimedia content through a bag-of-words approach. Finally, we have studied the proposed system on different scenarios by using vodcasts in English from various categories, as the targeted multimedia, and discussed how it would enhance people’s everyday life activities by different scenarios including education, entertainment, marketing, news and workplace