SST: Integrated Fluorocarbon Microsensor System Using Catalytic Modification

Selective, sensitive, and reliable sensors are urgently needed to detect air-borne halogenated volatile organic compounds (VOCs). This broad class of compounds includes chlorine, fluorine, bromine, and iodine containing hydrocarbons used as solvents, refrigerants, herbicides, and more recently as chemical warfare agents (CWAs). It is important to be able to detect very low concentrations of halocarbon solvents and insecticides because of their acute health effects even in very low concentrations. For instance, the nerve agent sarin (isopropyl methylphosphonofluoridate), first developed as an insecticide by German chemists in 1938, is so toxic that a ten minute exposure at an airborne concentration of only 65 parts per billion (ppb) can be fatal. Sarin became a household term when religious cult members on Tokyo subway trains poisoned over 5,500 people, killing 12. Sarin and other CWAs remain a significant threat to the health and safety of the general public. The goal of this project is to design a sensor system to detect and identify the composition and concentration of fluorinated VOCs. The system should be small, robust, compatible with metal oxide semiconductor (MOS) technology, cheap, if produced in large scale, and has the potential to be versatile in terms of low power consumption, detection of other gases, and integration in a portable system. The proposed VOC sensor system has three major elements that will be integrated into a microreactor flow cell: a temperature-programmable microhotplate array/reactor system which serves as the basic sensor platform; an innovative acoustic wave sensor, which detects material removal (instead of deposition) to verify and quantify the presence of fluorine; and an intelligent method, support vector machines, that will analyze the complex and high dimensional data furnished by the sensor system. The superior and complementary aspects of the three elements will be carefully integrated to create a system which is more sensitive and selective than other CWA detection systems that are commercially available or described in the research literature. While our sensor system will be developed to detect fluorinated VOCs, it can be adapted for other applications in which a target analyte can be catalytically converted for selective detection. Therefore, this investigation will examine the relationships between individual sensor element performance and joint sensor platform performance, integrated with state-of-the-art data analysis techniques. During development of the sensor system, the investigators will consider traditional reactor design concepts such as mass transfer and residence time effects, and will apply them to the emerging field of microsystems. The proposed research will provide the fundamental basis and understanding for examining multifunctional sensor platforms designed to provide extreme selectivity to targeted molecules. The project will involve interdisciplinary researchers and students and will connect to K-12 and RET programs for underrepresented students from rural areas

LMSFC: A Novel Multidimensional Index based on Learned Monotonic Space Filling Curves

The recently proposed learned indexes have attracted much attention as they can adapt to the actual data and query distributions to attain better search efficiency. Based on this technique, several existing works build up indexes for multi-dimensional data and achieve improved query performance. A common paradigm of these works is to (i) map multi-dimensional data points to a one-dimensional space using a fixed space-filling curve (SFC) or its variant and (ii) then apply the learned indexing techniques. We notice that the first step typically uses a fixed SFC method, such as row-major order and z-order. It definitely limits the potential of learned multi-dimensional indexes to adapt variable data distributions via different query workloads. In this paper, we propose a novel idea of learning a space-filling curve that is carefully designed and actively optimized for efficient query processing. We also identify innovative offline and online optimization opportunities common to SFC-based learned indexes and offer optimal and/or heuristic solutions. Experimental results demonstrate that our proposed method, LMSFC, outperforms state-of-the-art non-learned or learned methods across three commonly used real-world datasets and diverse experimental settings.Comment: Extended Version. Accepted by VLDB 202

Efficient bulk-loading methods for temporal and multidimensional index structures

Nahezu alle naturwissenschaftlichen Bereiche profitieren von neuesten Analyse- und Verarbeitungsmethoden für große Datenmengen. Diese Verfahren setzten eine effiziente Verarbeitung von geo- und zeitbezogenen Daten voraus, da die Zeit und die Position wichtige Attribute vieler Daten sind. Die effiziente Anfrageverarbeitung wird insbesondere durch den Einsatz von Indexstrukturen ermöglicht. Im Fokus dieser Arbeit liegen zwei Indexstrukturen: Multiversion B-Baum (MVBT) und R-Baum. Die erste Struktur wird für die Verwaltung von zeitbehafteten Daten, die zweite für die Indexierung von mehrdimensionalen Rechteckdaten eingesetzt. Ständig- und schnellwachsendes Datenvolumen stellt eine große Herausforderung an die Informatik dar. Der Aufbau und das Aktualisieren von Indexen mit herkömmlichen Methoden (Datensatz für Datensatz) ist nicht mehr effizient. Um zeitnahe und kosteneffiziente Datenverarbeitung zu ermöglichen, werden Verfahren zum schnellen Laden von Indexstrukturen dringend benötigt. Im ersten Teil der Arbeit widmen wir uns der Frage, ob es ein Verfahren für das Laden von MVBT existiert, das die gleiche I/O-Komplexität wie das externe Sortieren besitz. Bis jetzt blieb diese Frage unbeantwortet. In dieser Arbeit haben wir eine neue Kostruktionsmethode entwickelt und haben gezeigt, dass diese gleiche Zeitkomplexität wie das externe Sortieren besitzt. Dabei haben wir zwei algorithmische Techniken eingesetzt: Gewichts-Balancierung und Puffer-Bäume. Unsere Experimenten zeigen, dass das Resultat nicht nur theoretischer Bedeutung ist. Im zweiten Teil der Arbeit beschäftigen wir uns mit der Frage, ob und wie statistische Informationen über Geo-Anfragen ausgenutzt werden können, um die Anfrageperformanz von R-Bäumen zu verbessern. Unsere neue Methode verwendet Informationen wie Seitenverhältnis und Seitenlängen eines repräsentativen Anfragerechtecks, um einen guten R-Baum bezüglich eines häufig eingesetzten Kostenmodells aufzubauen. Falls diese Informationen nicht verfügbar sind, optimieren wir R-Bäume bezüglich der Summe der Volumina von minimal umgebenden Rechtecken der Blattknoten. Da das Problem des Aufbaus von optimalen R-Bäumen bezüglich dieses Kostenmaßes NP-hart ist, führen wir zunächst das Problem auf ein eindimensionales Partitionierungsproblem zurück, indem wir die Daten bezüglich optimierte raumfüllende Kurven sortieren. Dann lösen wir dieses Problem durch Einsatz vom dynamischen Programmieren. Die I/O-Komplexität des Verfahrens ist gleich der von externem Sortieren, da die I/O-Laufzeit der Methode durch die Laufzeit des Sortierens dominiert wird. Im letzten Teil der Arbeit haben wir die entwickelten Partitionierungsvefahren für den Aufbau von Geo-Histogrammen eingesetzt, da diese ähnlich zu R-Bäumen eine disjunkte Partitionierung des Raums erzeugen. Ergebnisse von intensiven Experimenten zeigen, dass sich unter Verwendung von neuen Partitionierungstechniken sowohl R-Bäume mit besserer Anfrageperformanz als auch Geo-Histogrammen mit besserer Schätzqualität im Vergleich zu Konkurrenzverfahren generieren lassen

Scalable data retrieval in a mobile environment

Retrieving multidimensional data out of distributed systems becomes increasingly important. But applications of these systems are often not only interested in data vectors that match certain queries. Instead, many applications demand for retrieval of data with high quality. In this thesis, we design a distributed system that can be used by applications to retrieve data of high quality for arbitrary multidimensional queries. Major challenges for the quality-based data retrieval are to 1.) find an appropriate formalization of data quality, 2.) design routing algorithms for queries, that are robust in the presence of high dynamics with respect to the participants of the system and the data on the participants and 3.) handle heterogeneous and high-dimensional data in the system. In order to retrieve data quality, we propose 1.) the measure of confidence for a query that is based on clusters of data. When a participant of the system finds, that its confidence for a query is high, it will assume to possess data of high quality for that query. 2.) Further, we design and implement routing strategies in order to route queries to nodes that can answer them with high confidence. Maintaining exact routing tables for each possible query would be infeasible, so nodes have to model the data that can be reached via neighbours in routing models. Such modelling of data is based on structural properties of the data such as how good the data can be clustered. 3.) In the high-dimensional space, we have to overcome the curse of dimensionality: the structure of data can become invisible in higher dimensions. We address this problem with a method for dimensionality reduction that reduces the dimensions with highest data variance. The evaluation of our approaches shows a high accuracy of query routing, even if our approaches do not make use of scalability bottlenecks like flooding of the query or flooding of routing information. Further, we show that the use of dimensionality reduction in routing has positive influence on the routing accuracy. We think that the methods in our approach can be useful instruments, whenever the task of retrieving data of high quality has to be outsourced to a distributed system

6 Access Methods and Query Processing Techniques

The performance of a database management system (DBMS) is fundamentally dependent on the access methods and query processing techniques available to the system. Traditionally, relational DBMSs have relied on well-known access methods, such as the ubiquitous B +-tree, hashing with chaining, and, in som

Design and Implementation of a Middleware for Uniform, Federated and Dynamic Event Processing

In recent years, real-time processing of massive event streams has become an important topic in the area of data analytics. It will become even more important in the future due to cheap sensors, a growing amount of devices and their ubiquitous inter-connection also known as the Internet of Things (IoT). Academia, industry and the open source community have developed several event processing (EP) systems that allow users to define, manage and execute continuous queries over event streams. They achieve a significantly better performance than the traditional store-then-process'' approach in which events are first stored and indexed in a database. Because EP systems have different roots and because of the lack of standardization, the system landscape became highly heterogenous. Today's EP systems differ in APIs, execution behaviors and query languages. This thesis presents the design and implementation of a novel middleware that abstracts from different EP systems and provides a uniform API, execution behavior and query language to users and developers. As a consequence, the presented middleware overcomes the problem of vendor lock-in and different EP systems are enabled to cooperate with each other. In practice, event streams differ dramatically in volume and velocity. We show therefore how the middleware can connect to not only different EP systems, but also database systems and a native implementation. Emerging applications such as the IoT raise novel challenges and require EP to be more dynamic. We present extensions to the middleware that enable self-adaptivity which is needed in context-sensitive applications and those that deal with constantly varying sets of event producers and consumers. Lastly, we extend the middleware to fully support the processing of events containing spatial data and to be able to run distributed in the form of a federation of heterogenous EP systems