Twitter's popularity as a source of up-to-date news and information is constantly increasing. In response to this trend, numerous event detection techniques have been proposed to cope with the rate and volume of Twitter data streams. Although most of these works conduct some evaluation of the proposed technique, a comparative study is often omitted. In this paper, we present a survey and experimental analysis of state-of-the-art event detection techniques for Twitter data streams. In order to conduct this study, we define a series of measures to support the quantitative and qualitative comparison. We demonstrate the effectiveness of these measures by applying them to event detection techniques as well as to baseline approaches using real-world Twitter streaming data.

A key promise of SQL is that the optimizer will find the most efficient execution plan, regardless of how the query is formulated. In general, query optimizers of modern database systems are able to keep this promise, with the notable exception of nested queries. While several optimization techniques for nested queries have been proposed, their adoption in practice has been limited. In this paper, we argue that the NF2 (non-first normal form) algebra, which was originally designed to process nested tables, is a better approach to nested query optimization as it fulfills two key requirements. First, the NF2 algebra can represent all types of nested queries as well as both existing and novel optimization techniques based on its equivalences. Second, performance benefits can be achieved with little changes to existing transformation-based query optimizers as the NF2 algebra is an extension of the relational algebra.

In order to manage XML documents, native XML databases use specific encodings that map the hierarchical structure of a document to a flat representation. Several encodings have been proposed that differ in terms of their support for certain query workloads. While some encodings are optimized for query processing, others focus on data manipulation. For example, the Pre/Dist/Size XML encoding has been designed to support queries over all XPath axes efficiently, but processing atomic updates in XML documents can be costly. In this paper, we present a technique, so-called structural bulk updates, that works in concert with the XQuery Update Facility to support efficient updates on the Pre/Dist/Size encoding. We demonstrate the benefits of our technique in a detailed performance evaluation based on the XMark benchmark.

Social networks are platforms where millions of users interact frequently and share variety of digital content with each other. Users express their feelings and opinions on every topic of interest. These opinions carry import value for personal, academic and commercial applications, but the volume and the speed at which these are produced make it a challenging task for researchers and the underlying technologies to provide useful insights to such data. We attempt to extend the established OLAP(On-line Analytical Processing) technology to allow multidimensional analysis of social media data by integrating text and opinion mining methods into the data warehousing system and by exploiting various knowledge discovery techniques to deal with semi-structured and unstructured data from social media.

In the recent year Twitter has evolved into an extremely popular social network and has revolutionized the ways of interacting and exchanging information on the Internet. By making its public stream available through a set of APIs Twitter has triggered a wave of research initiatives aimed at analysis and knowledge discovery from the data about its users and their messaging activities. While most of the projects and tools are tailored towards solving specific tasks, we pursue a goal of providing an application in dependent and universal analytical platform for supporting any kind of analysis and knowledge discovery. We employ the well established data warehousing technology with its underlying multidimensional data model, ETL routine for loading and consolidating data from different sources, OLAP functionality for exploring the data and data mining tools for more sophisticated analysis. In this work we describe the process of transforming the original stream into a set of related multidimensional cubes and demonstrate how the resulting data warehouse can be used for solving a variety of analytical tasks. We expect our proposed approach to be applicable for analyzing the data of other social networks as well.

A key difference between traditional humanities research and the emerging field of digital humanities is that the latter aims to complement qualitative methods with quantitative data. In linguistics, this means the use of large corpora of text, which are usually annotated automatically using natural language processing tools. However, these tools do not exist for historical texts, so scholars have to work with unannotated data. We have developed a system for systematic, iterative exploration and annotation of historical text corpora, which relies on an XML database (BaseX) and in particular on the Full Text and Update facilities of XQuery.

With the standard OLAP technology, cubes are constructed from the input data based on the available data fields and known relationships between them. Structuring the data into a set of numeric measures distributed along a set of uniformly structured dimensions may be unrealistic for applications dealing with semi-structured data. We propose to extend the capabilities of OLAP via content-driven discovery of measures and dimensional characteristics in the original dataset. New structural elements are discovered by means of data mining and other techniques and are therefore prone to changes as the underlying dataset evolves. In this work we focus on the challenge of generating, maintaining, and querying such discovered elements of the cube.

Mere storage of personal data in state-of-the-art filesystems is a markedly well done job in current operating systems. Convenient access to and information retrieval from such data, however, is crucial to leverage the stored information. Thereby database style query languages can be of great use. We demonstrate a user level filesystem implementation that is built on recent semi-structured database storage techniques. As such, it serves as a storage layer for the BaseX XQuery processor and, while it appears to the operating system as a conventional filesystem, a large part of its content can be queried using XPath/XQuery.

Analysis of surgical procedures is an emerging domain of medical engineering aimed at advancing surgical assist systems. The term Surgical Workflows is used for describing the methodical framework for acquiring formal descriptions of surgical interventions. A formal model and a uniform recording scheme of a surgical process are crucial for systematic accumulation of relevant data from running surgical instances in a form, appropriate for analysis. This paper describes the process of designing a data warehouse for surgical workflow analysis. Data warehousing technology, originally developed for quantitative business data analysis for the purpose of decision making, is adaptable to the requirements of surgical data and analysis. We describe a conceptual model of a surgical procedure obtained in accordance with the multidimensional data model. We demonstrate how a subject-oriented multidimensional perspective of a surgery and its components enables powerful analysis and exploration by defining the metrics of interests and aggregating those metrics along various dimensions and levels of details. Apart from its primary function, i.e. quantitative analysis, the data of the surgical data warehouse may serve as input for other data-intensive systems, e.g. visualization and data mining tools.

With the rise of XML, the database community has been challenged by semi-structured data processing. Since the data type behind XML is the tree, state-of-the-art RDBMSs have learned to deal with such data ( e. g. , [18, 5, 6, 16]). This paper introduces a Ph.D. project focused on the question in how far the tree-awareness of recent RDBMSs can be used to, once again, try to implement filesystems using database technology. Our main goal is to provide means to query the data stored in filesystems and to find ways to enhance/combine the data storage and query capabilities of operating systems using semi-structured database technology. Two DBMSs with relational XML storage, built on top of the XPath accelerator numbering scheme [14], are the foundations for our work. With BaseX, an XML database, we establish a link between user, database and filesystem content. BaseX allows visual access to filesystem data stored in the database. An integrated query interface allows users to filter query results in interactive response time. Second, we establish a link between DBMS and OS. We implement a filesystem in userspace backed by the MonetDB/XQuery system, a well-known relational database system, which integrates the Pathfinder XQuery compiler [5] and the MonetDB kernel [4]. As a result, the DBMS is mounted as a conventional filesystem by the operating system kernel. Consequently, access via the established (virtual) filesystem interface as well as database enhanced access to the same data is provided.

An iterator is proposed that allows sequences of update operations to be applied in a set-oriented way with deterministic semantics. Because the mechanism is independent of a particular model, it can be used in the relational and in object-oriented ones. Thus, the deterministic semantics of embedded structured query language (SQL) cursors and of triggers that are applied after (set-oriented) SQL updates can be checked. The iterator can be used to apply object-oriented methods, which are usually update sequences defined on a single object, to sets in a deterministic way.>

In order to obtain the performance required for nonstandard database environments, a hierarchical complex object model with object references is used as a storage structure for complex objects. Several storage models for these complex objects, as well as a benchmark to evaluate their performance, are described. A cost model for analytical performance evaluation is developed, and the analytical results are validated by means of measurements on the DASDBS, complex object storage system. The results show which storage structures for complex objects are the most efficient under which circumstances.>

A retrospective of the Darmstadt database system project, also known as DASDBS, is presented. The project is aimed at providing data management support for advanced applications, such as geo-scientific information systems and office automation. Similar to the dichotomy of RSS and RDS in System R, a layered architectural approach was pursued: a storage management kernel serves as the lowest common denominator of the requirements of the various applications classes, and a family of application-oriented front-ends provides semantically richer functions on top of the kernel. The lessons that were learned from building the DASDBS system are discussed. Particular emphasis is placed on the following issues: the role of nested relations, the experiences with using object buffers for coupling the system with the programming-language environment and the learning process in implementing multilevel transactions.>

The multi-layered architecture of the DArmStadt Data Base System (DASDBS) for advanced applications is introduced DASDBS is conceived as a family of application-specific database systems on top of a common database kernel system. The main design problem considered here is, What features are common enough to be integrated into the kernel and what features are rather application-specific? Kernel features must be simple enough to be efficiently implemented and to serve a broad class of clients, yet powerful enough to form a convenient basis for application-oriented layers. Our kernel provides mechanisms to efficiently store hierarchically structured complex objects, and offers operations which are set-oriented and can be processed in a single scan through the objects. To achieve high concurrency in a layered system, a multi-level transaction methodology is applied. First experiences with our current implementation and some lessons we have learned from it are reported.