Managing Unbounded-Length Keys in Comparison-Driven Data Structures with Applications to On-Line Indexing

This paper presents a general technique for optimally transforming any dynamic data structure that operates on atomic and indivisible keys by constant-time comparisons, into a data structure that handles unbounded-length keys whose comparison cost is not a constant. Examples of these keys are strings, multi-dimensional points, multiple-precision numbers, multi-key data (e.g.~records), XML paths, URL addresses, etc. The technique is more general than what has been done in previous work as no particular exploitation of the underlying structure of is required. The only requirement is that the insertion of a key must identify its predecessor or its successor. Using the proposed technique, online suffix tree can be constructed in worst case time $O(\log n)$ per input symbol (as opposed to amortized $O(\log n)$ time per symbol, achieved by previously known algorithms). To our knowledge, our algorithm is the first that achieves $O(\log n)$ worst case time per input symbol. Searching for a pattern of length $m$ in the resulting suffix tree takes $O(\min(m\log |\Sigma|, m + \log n) + tocc)$ time, where $tocc$ is the number of occurrences of the pattern. The paper also describes more applications and show how to obtain alternative methods for dealing with suffix sorting, dynamic lowest common ancestors and order maintenance

Scalable Architecture for Integrated Batch and Streaming Analysis of Big Data

Thesis (Ph.D.) - Indiana University, Computer Sciences, 2015As Big Data processing problems evolve, many modern applications demonstrate special characteristics. Data exists in the form of both large historical datasets and high-speed real-time streams, and many analysis pipelines require integrated parallel batch processing and stream processing. Despite the large size of the whole dataset, most analyses focus on specific subsets according to certain criteria. Correspondingly, integrated support for efficient queries and post- query analysis is required. To address the system-level requirements brought by such characteristics, this dissertation proposes a scalable architecture for integrated queries, batch analysis, and streaming analysis of Big Data in the cloud. We verify its effectiveness using a representative application domain - social media data analysis - and tackle related research challenges emerging from each module of the architecture by integrating and extending multiple state-of-the-art Big Data storage and processing systems. In the storage layer, we reveal that existing text indexing techniques do not work well for the unique queries of social data, which put constraints on both textual content and social context. To address this issue, we propose a flexible indexing framework over NoSQL databases to support fully customizable index structures, which can embed necessary social context information for efficient queries. The batch analysis module demonstrates that analysis workflows consist of multiple algorithms with different computation and communication patterns, which are suitable for different processing frameworks. To achieve efficient workflows, we build an integrated analysis stack based on YARN, and make novel use of customized indices in developing sophisticated analysis algorithms. In the streaming analysis module, the high-dimensional data representation of social media streams poses special challenges to the problem of parallel stream clustering. Due to the sparsity of the high-dimensional data, traditional synchronization method becomes expensive and severely impacts the scalability of the algorithm. Therefore, we design a novel strategy that broadcasts the incremental changes rather than the whole centroids of the clusters to achieve scalable parallel stream clustering algorithms. Performance tests using real applications show that our solutions for parallel data loading/indexing, queries, analysis tasks, and stream clustering all significantly outperform implementations using current state-of-the-art technologies

Bridging the gap between algorithmic and learned index structures

Index structures such as B-trees and bloom filters are the well-established petrol engines of database systems. However, these structures do not fully exploit patterns in data distribution. To address this, researchers have suggested using machine learning models as electric engines that can entirely replace index structures. Such a paradigm shift in data system design, however, opens many unsolved design challenges. More research is needed to understand the theoretical guarantees and design efficient support for insertion and deletion. In this thesis, we adopt a different position: index algorithms are good enough, and instead of going back to the drawing board to fit data systems with learned models, we should develop lightweight hybrid engines that build on the benefits of both algorithmic and learned index structures. The indexes that we suggest provide the theoretical performance guarantees and updatability of algorithmic indexes while using position prediction models to leverage the data distributions and thereby improve the performance of the index structure. We investigate the potential for minimal modifications to algorithmic indexes such that they can leverage data distribution similar to how learned indexes work. In this regard, we propose and explore the use of helping models that boost classical index performance using techniques from machine learning. Our suggested approach inherits performance guarantees from its algorithmic baseline index, but at the same time it considers the data distribution to improve performance considerably. We study single-dimensional range indexes, spatial indexes, and stream indexing, and show that the suggested approach results in range indexes that outperform the algorithmic indexes and have comparable performance to the read-only, fully learned indexes and hence can be reliably used as a default index structure in a database engine. Besides, we consider the updatability of the indexes and suggest solutions for updating the index, notably when the data distribution drastically changes over time (e.g., for indexing data streams). In particular, we propose a specific learning-augmented index for indexing a sliding window with timestamps in a data stream. Additionally, we highlight the limitations of learned indexes for low-latency lookup on real- world data distributions. To tackle this issue, we suggest adding an algorithmic enhancement layer to a learned model to correct the prediction error with a small memory latency. This approach enables efficient modelling of the data distribution and resolves the local biases of a learned model at the cost of roughly one memory lookup.Open Acces

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

High-performance online spatial and temporal aggregations on multi-core CPUs and many-core GPUs, in:

a b s t r a c t With the increasing availability of locating and navigation technologies on portable wireless devices, huge amounts of location data are being captured at ever growing rates. Spatial and temporal aggregations in an Online Analytical Processing (OLAP) setting for the large-scale ubiquitous urban sensing data play an important role in understanding urban dynamics and facilitating decision making. Unfortunately, existing spatial, temporal and spatiotemporal OLAP techniques are mostly based on traditional computing frameworks, i.e., disk-resident systems on uniprocessors based on serial algorithms, which makes them incapable of handling largescale data on parallel hardware architectures that have already been equipped with commodity computers. In this study, we report our designs, implementations and experiments on developing a data management platform and a set of parallel techniques to support highperformance online spatial and temporal aggregations on multi-core CPUs and many-core Graphics Processing Units (GPUs). Our experiment results show that we are able to spatially associate nearly 170 million taxi pickup location points with their nearest street segments among 147,011 candidates in about 5-25 s on both an Nvidia Quadro 6000 GPU device and dual Intel Xeon E5405 quad-core CPUs when their Vector Processing Units (VPUs) are utilized for computing intensive tasks. After spatially associating points with road segments, spatial, temporal and spatiotemporal aggregations are reduced to relational aggregations and can be processed in the order of a fraction of a second on both GPUs and multi-core CPUs. In addition to demonstrating the feasibility of building a high-performance OLAP system for processing large-scale taxi trip data for real-time, interactive data explorations, our work also opens the paths to achieving even higher OLAP query efficiency for large-scale applications through integrating domain-specific data management platforms, novel parallel data structures and algorithm designs, and hardware architecture friendly implementations

The Online House Numbering Problem: Min-Max Online List Labeling

We introduce and study the online house numbering problem, where houses are added arbitrarily along a road and must be assigned labels to maintain their ordering along the road. The online house numbering problem is related to classic online list labeling problems, except that the optimization goal here is to minimize the maximum number of times that any house is relabeled. We provide several algorithms that achieve interesting tradeoffs between upper bounds on the number of maximum relabels per element and the number of bits used by labels

Survey on securing data storage in the cloud

Cloud Computing has become a well-known primitive nowadays; many researchers and companies are embracing this fascinating technology with feverish haste. In the meantime, security and privacy challenges are brought forward while the number of cloud storage user increases expeditiously. In this work, we conduct an in-depth survey on recent research activities of cloud storage security in association with cloud computing. After an overview of the cloud storage system and its security problem, we focus on the key security requirement triad, i.e., data integrity, data confidentiality, and availability. For each of the three security objectives, we discuss the new unique challenges faced by the cloud storage services, summarize key issues discussed in the current literature, examine, and compare the existing and emerging approaches proposed to meet those new challenges, and point out possible extensions and futuristic research opportunities. The goal of our paper is to provide a state-of-the-art knowledge to new researchers who would like to join this exciting new field

Fachlich erweiterbare 3D-Stadtmodelle – Management, Visualisierung und Interaktion

Domain-extendable semantic 3D city models are complex mappings and inventories of the urban environment which can be utilized as an integrative information backbone to facilitate a range of application fields like urban planning, environmental simulations, disaster management, and energy assessment. Today, more and more countries and cities worldwide are creating their own 3D city models based on the CityGML specification which is an international standard issued by the Open Geospatial Consortium (OGC) to provide an open data model and XML-based format for describing the relevant urban objects with regards to their 3D geometry, topology, semantics, and appearance. It especially provides a flexible and systematic extension mechanism called “Application Domain Extension (ADE)” which allows third parties to dynamically extend the existing CityGML definitions with additional information models from different application domains for representing the extended or newly introduced geographic object types within a common framework. However, due to the consequent large size and high model complexity, the practical utilization of country-wide CityGML datasets has posed a tremendous challenge regarding the setup of an extensive application system to support the efficient data storage, analysis, management, interaction, and visualization. These requirements have been partly solved by the existing free 3D geo-database solution called ‘3D City Database (3DCityDB)’ which offers a rich set of functionalities for dealing with standard CityGML data models, but lacked the support for CityGML ADEs. The key motivation of this thesis is to develop a reliable approach for extending the existing database solution to support the efficient management, visualization, and interaction of large geospatial data elements of arbitrary CityGML ADEs. Emphasis is first placed on answering the question of how to dynamically extend the relational database schema by parsing and interpreting the XML schema files of the ADE and dynamically create new database tables accordingly. Based on a comprehensive survey of the related work, a new graph-based framework has been proposed which uses typed and attributed graphs for semantically representing the object-oriented data models of CityGML ADEs and utilizes graph transformation systems to automatically generate compact table structures extending the 3DCityDB. The transformation process is performed by applying a series of fine-grained graph transformation rules which allow users to declaratively describe the complex mapping rules including the optimization concepts that are employed in the development of the 3DCityDB database schema. The second major contribution of this thesis is the development of a new multi-level system which can serve as a complete and integrative platform for facilitating the various analysis, simulation, and modification operations on the complex-structured 3D city models based on CityGML and 3DCityDB. It introduces an additional application level based on a so-called ‘app-concept’ that allows for constructing a light-weight web application to reach a good balance between the high data model complexity and the specific application requirements of the end users. Each application can be easily built on top of a developed 3D web client whose functionalities go beyond the efficient 3D geo-visualization and interactive exploration, and also allows for performing collaborative modifications and analysis of 3D city models by taking advantage of the Cloud Computing technology. This multi-level system along with the extended 3DCityDB have been successfully utilized and evaluated by many practical projects.Fachlich erweiterbare semantische 3D-Stadtmodelle sind komplexe Abbildungen und Datenbestände der städtischen Umgebung, die als ein integratives Informationsrückgrat genutzt werden können, um eine Reihe von Anwendungsfeldern wie z. B. Stadtplanung, Umweltsimulationen, Katastrophenmanagement und Energiebewertung zu ermöglichen. Heute schaffen immer mehr Länder und Städte weltweit ihre eigenen 3D-Stadtmodelle auf Basis des internationalen Standards CityGML des Open Geospatial Consortium (OGC), um ein offenes Datenmodell und ein XML-basiertes Format zur Beschreibung der relevanten Stadtobjekte in Bezug auf ihre 3D-Geometrien, Topologien, Semantik und Erscheinungen zur Verfügung zu stellen. Es bietet insbesondere einen flexiblen und systematischen Erweiterungsmechanismus namens „Application Domain Extension“ (ADE), der es Dritten ermöglicht, die bestehenden CityGML-Definitionen mit zusätzlichen Informationsmodellen aus verschiedenen Anwendungsdomänen dynamisch zu erweitern, um die erweiterten oder neu eingeführten Stadtobjekt-Typen innerhalb eines gemeinsamen Framework zu repräsentieren. Aufgrund der konsequent großen Datenmenge und hohen Modellkomplexität bei der praktischen Nutzung der landesweiten CityGML-Datensätze wurden jedoch enorme Anforderungen an den Aufbau eines umfangreichen Anwendungssystems zur Unterstützung der effizienten Speicherung, Analyse, Verwaltung, Interaktion und Visualisierung der Daten gestellt. Die bestehende kostenlose 3D-Geodatenbank-Lösung „3D City Database“ (3DCityDB) entsprach bereits teilweise diesen Anforderungen, indem sie zwar eine umfangreiche Funktionalität für den Umgang mit den Standard-CityGML-Datenmodellen, jedoch keine Unterstützung für CityGML-ADEs bietet. Die Schlüsselmotivation für diese Arbeit ist es, einen zuverlässigen Ansatz zur Erweiterung der bestehenden Datenbanklösung zu entwickeln, um das effiziente Management, die Visualisierung und Interaktion großer Datensätze beliebiger CityGML-ADEs zu unterstützen. Der Schwerpunkt liegt zunächst auf der Beantwortung der Schlüsselfrage, wie man das relationale Datenbankschema dynamisch erweitern kann, indem die XML-Schemadateien der ADE analysiert und interpretiert und anschließend dem entsprechende neue Datenbanktabellen erzeugt werden. Auf Grundlage einer umfassenden Studie verwandter Arbeiten wurde ein neues graphbasiertes Framework entwickelt, das die typisierten und attributierten Graphen zur semantischen Darstellung der objektorientierten Datenmodelle von CityGML-ADEs verwendet und anschließend Graphersetzungssysteme nutzt, um eine kompakte Tabellenstruktur zur Erweiterung der 3DCityDB zu generieren. Der Transformationsprozess wird durch die Anwendung einer Reihe feingranularer Graphersetzungsregeln durchgeführt, die es Benutzern ermöglicht, die komplexen Mapping-Regeln einschließlich der Optimierungskonzepte aus der Entwicklung des 3DCityDB-Datenbankschemas deklarativ zu formalisieren. Der zweite wesentliche Beitrag dieser Arbeit ist die Entwicklung eines neuen mehrstufigen Systemkonzepts, das auf CityGML und 3DCityDB basiert und gleichzeitig als eine komplette und integrative Plattform zur Erleichterung der Analyse, Simulationen und Modifikationen der komplex strukturierten 3D-Stadtmodelle dienen kann. Das Systemkonzept enthält eine zusätzliche Anwendungsebene, die auf einem sogenannten „App-Konzept“ basiert, das es ermöglicht, eine leichtgewichtige Applikation bereitzustellen, die eine gute Balance zwischen der hohen Modellkomplexität und den spezifischen Anwendungsanforderungen der Endbenutzer erreicht. Jede Applikation lässt sich ganz einfach mittels eines bereits entwickelten 3D-Webclients aufbauen, dessen Funktionalitäten über die effiziente 3D-Geo-Visualisierung und interaktive Exploration hinausgehen und auch die Durchführung kollaborativer Modifikationen und Analysen von 3D-Stadtmodellen mit Hilfe von der Cloud-Computing-Technologie ermöglichen. Dieses mehrstufige System zusammen mit dem erweiterten 3DCityDB wurde erfolgreich in vielen praktischen Projekten genutzt und bewertet

A Survey of Distributed Data Stream Processing Frameworks

Big data processing systems are evolving to be more stream oriented where each data record is processed as it arrives by distributed and low-latency computational frameworks on a continuous basis. As the stream processing technology matures and more organizations invest in digital transformations, new applications of stream analytics will be identified and implemented across a wide spectrum of industries. One of the challenges in developing a streaming analytics infrastructure is the difficulty in selecting the right stream processing framework for the different use cases. With a view to addressing this issue, in this paper we present a taxonomy, a comparative study of distributed data stream processing and analytics frameworks, and a critical review of representative open source (Storm, Spark Streaming, Flink, Kafka Streams) and commercial (IBM Streams) distributed data stream processing frameworks. The study also reports our ongoing study on a multilevel streaming analytics architecture that can serve as a guide for organizations and individuals planning to implement a real-time data stream processing and analytics framework

Efficient Indexing and Query Processing of Model-View Sensor Data in the Cloud

As the number of sensors that pervade our lives increases (e.g., environmental sensors, phone sensors, etc.), the efficient management of massive amount of sensor data is becoming increasingly important. The infinite nature of sensor data poses a serious challenge for query processing even in a cloud infrastructure. Traditional raw sensor data management systems based on relational databases lack scalability to accommodate large-scale sensor data efficiently. Thus, distributed key-value stores in the cloud are becoming a prime tool to manage sensor data. Model-view sensor data management, which stores the sensor data in the form of modeled segments, brings the additional advantages of data compression and value interpolation. However, currently there are no techniques for indexing and/or query optimization of the model-view sensor data in the cloud; full table scan is needed for query processing in the worst case. In this paper, we propose an innovative index for modeled segments in key-value stores, namely KVI-index. KVI-index consists of two interval indices on the time and sensor value dimensions respectively, each of which has an in-memory search tree and a secondary list materialized in the key-value store. Then, we introduce a KVI-index–Scan–MapReduce hybrid approach to perform efficient query processing upon modeled data streams. As proved by a series of experiments at a private cloud infrastructure, our approach outperforms in query-response time and index-updating efficiency both Hadoop-based parallel processing of the raw sensor data and multiple alternative indexing approaches of model-view data