Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds

We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbols to modal representations of beliefs, knowledge, and experience external to the linguistic system. This Indexical Model incorporates multiple information sources, including perceptions, domain knowledge, and short-term and long-term experiences during comprehension. We show that exploiting diverse information sources can alleviate ambiguities that arise from contextual use of underspecific referring expressions and unexpressed argument alternations of verbs. The model is being used to support linguistic interactions in Rosie, an agent implemented in Soar that learns from instruction.Comment: Advances in Cognitive Systems 3 (2014

Complex queries and complex data

With the widespread availability of wearable computers, equipped with sensors such as GPS or cameras, and with the ubiquitous presence of micro-blogging platforms, social media sites and digital marketplaces, data can be collected and shared on a massive scale. A necessary building block for taking advantage from this vast amount of information are efficient and effective similarity search algorithms that are able to find objects in a database which are similar to a query object. Due to the general applicability of similarity search over different data types and applications, the formalization of this concept and the development of strategies for evaluating similarity queries has evolved to an important field of research in the database community, spatio-temporal database community, and others, such as information retrieval and computer vision. This thesis concentrates on a special instance of similarity queries, namely k-Nearest Neighbor (kNN) Queries and their close relative, Reverse k-Nearest Neighbor (RkNN) Queries. As a first contribution we provide an in-depth analysis of the RkNN join. While the problem of reverse nearest neighbor queries has received a vast amount of research interest, the problem of performing such queries in a bulk has not seen an in-depth analysis so far. We first formalize the RkNN join, identifying its monochromatic and bichromatic versions and their self-join variants. After pinpointing the monochromatic RkNN join as an important and interesting instance, we develop solutions for this class, including a self-pruning and a mutual pruning algorithm. We then evaluate these algorithms extensively on a variety of synthetic and real datasets. From this starting point of similarity queries on certain data we shift our focus to uncertain data, addressing nearest neighbor queries in uncertain spatio-temporal databases. Starting from the traditional definition of nearest neighbor queries and a data model for uncertain spatio-temporal data, we develop efficient query mechanisms that consider temporal dependencies during query evaluation. We define intuitive query semantics, aiming not only at returning the objects closest to the query but also their probability of being a nearest neighbor. After theoretically evaluating these query predicates we develop efficient querying algorithms for the proposed query predicates. Given the findings of this research on nearest neighbor queries, we extend these results to reverse nearest neighbor queries. Finally we address the problem of querying large datasets containing set-based objects, namely image databases, where images are represented by (multi-)sets of vectors and additional metadata describing the position of features in the image. We aim at reducing the number of kNN queries performed during query processing and evaluate a modified pipeline that aims at optimizing the query accuracy at a small number of kNN queries. Additionally, as feature representations in object recognition are moving more and more from the real-valued domain to the binary domain, we evaluate efficient indexing techniques for binary feature vectors.Nicht nur durch die Verbreitung von tragbaren Computern, die mit einer Vielzahl von Sensoren wie GPS oder Kameras ausgestattet sind, sondern auch durch die breite Nutzung von Microblogging-Plattformen, Social-Media Websites und digitale Marktplätze wie Amazon und Ebay wird durch die User eine gigantische Menge an Daten veröffentlicht. Um aus diesen Daten einen Mehrwert erzeugen zu können bedarf es effizienter und effektiver Algorithmen zur Ähnlichkeitssuche, die zu einem gegebenen Anfrageobjekt ähnliche Objekte in einer Datenbank identifiziert. Durch die Allgemeinheit dieses Konzeptes der Ähnlichkeit über unterschiedliche Datentypen und Anwendungen hinweg hat sich die Ähnlichkeitssuche zu einem wichtigen Forschungsfeld, nicht nur im Datenbankumfeld oder im Bereich raum-zeitlicher Datenbanken, sondern auch in anderen Forschungsgebieten wie dem Information Retrieval oder dem Maschinellen Sehen entwickelt. In der vorliegenden Arbeit beschäftigen wir uns mit einem speziellen Anfrageprädikat im Bereich der Ähnlichkeitsanfragen, mit k-nächste Nachbarn (kNN) Anfragen und ihrem Verwandten, den Revers k-nächsten Nachbarn (RkNN) Anfragen. In einem ersten Beitrag analysieren wir den RkNN Join. Obwohl das Problem von reverse nächsten Nachbar Anfragen in den letzten Jahren eine breite Aufmerksamkeit in der Forschungsgemeinschaft erfahren hat, wurde das Problem eine Menge von RkNN Anfragen gleichzeitig auszuführen nicht ausreichend analysiert. Aus diesem Grund formalisieren wir das Problem des RkNN Joins mit seinen monochromatischen und bichromatischen Varianten. Wir identifizieren den monochromatischen RkNN Join als einen wichtigen und interessanten Fall und entwickeln entsprechende Anfragealgorithmen. In einer detaillierten Evaluation vergleichen wir die ausgearbeiteten Verfahren auf einer Vielzahl von synthetischen und realen Datensätzen. Nach diesem Kapitel über Ähnlichkeitssuche auf sicheren Daten konzentrieren wir uns auf unsichere Daten, speziell im Bereich raum-zeitlicher Datenbanken. Ausgehend von der traditionellen Definition von Nachbarschaftsanfragen und einem Datenmodell für unsichere raum-zeitliche Daten entwickeln wir effiziente Anfrageverfahren, die zeitliche Abhängigkeiten bei der Anfragebearbeitung beachten. Zu diesem Zweck definieren wir Anfrageprädikate die nicht nur die Objekte zurückzugeben, die dem Anfrageobjekt am nächsten sind, sondern auch die Wahrscheinlichkeit mit der sie ein nächster Nachbar sind. Wir evaluieren die definierten Anfrageprädikate theoretisch und entwickeln effiziente Anfragestrategien, die eine Anfragebearbeitung zu vertretbaren Laufzeiten gewährleisten. Ausgehend von den Ergebnissen für Nachbarschaftsanfragen erweitern wir unsere Ergebnisse auf Reverse Nachbarschaftsanfragen. Zuletzt behandeln wir das Problem der Anfragebearbeitung bei Mengen-basierten Objekten, die zum Beispiel in Bilddatenbanken Verwendung finden: Oft werden Bilder durch eine Menge von Merkmalsvektoren und zusätzliche Metadaten (zum Beispiel die Position der Merkmale im Bild) dargestellt. Wir evaluieren eine modifizierte Pipeline, die darauf abzielt, die Anfragegenauigkeit bei einer kleinen Anzahl an kNN-Anfragen zu maximieren. Da reellwertige Merkmalsvektoren im Bereich der Objekterkennung immer öfter durch Bitvektoren ersetzt werden, die sich durch einen geringeren Speicherplatzbedarf und höhere Laufzeiteffizienz auszeichnen, evaluieren wir außerdem Indexierungsverfahren für Binärvektoren

Efficient processing of large-scale spatio-temporal data

Millionen Geräte, wie z.B. Mobiltelefone, Autos und Umweltsensoren senden ihre Positionen zusammen mit einem Zeitstempel und weiteren Nutzdaten an einen Server zu verschiedenen Analysezwecken. Die Positionsinformationen und übertragenen Ereignisinformationen werden als Punkte oder Polygone dargestellt. Eine weitere Art räumlicher Daten sind Rasterdaten, die zum Beispiel von Kameras und Sensoren produziert werden. Diese großen räumlich-zeitlichen Datenmengen können nur auf skalierbaren Plattformen wie Hadoop und Apache Spark verarbeitet werden, die jedoch z.B. die Nachbarschaftsinformation nicht ausnutzen können - was die Ausführung bestimmter Anfragen praktisch unmöglich macht. Die wiederholten Ausführungen der Analyseprogramme während ihrer Entwicklung und durch verschiedene Nutzer resultieren in langen Ausführungszeiten und hohen Kosten für gemietete Ressourcen, die durch die Wiederverwendung von Zwischenergebnissen reduziert werden können. Diese Arbeit beschäftigt sich mit den beiden oben beschriebenen Herausforderungen. Wir präsentieren zunächst das STARK Framework für die Verarbeitung räumlich-zeitlicher Vektor- und Rasterdaten in Apache Spark. Wir identifizieren verschiedene Algorithmen für Operatoren und analysieren, wie diese von den Eigenschaften der zugrundeliegenden Plattform profitieren können. Weiterhin wird untersucht, wie Indexe in der verteilten und parallelen Umgebung realisiert werden können. Außerdem vergleichen wir Partitionierungsmethoden, die unterschiedlich gut mit ungleichmäßiger Datenverteilung und der Größe der Datenmenge umgehen können und präsentieren einen Ansatz um die auf Operatorebene zu verarbeitende Datenmenge frühzeitig zu reduzieren. Um die Ausführungszeit von Programmen zu verkürzen, stellen wir einen Ansatz zur transparenten Materialisierung von Zwischenergebnissen vor. Dieser Ansatz benutzt ein Entscheidungsmodell, welches auf den tatsächlichen Operatorkosten basiert. In der Evaluierung vergleichen wir die verschiedenen Implementierungs- sowie Konfigurationsmöglichkeiten in STARK und identifizieren Szenarien wann Partitionierung und Indexierung eingesetzt werden sollten. Außerdem vergleichen wir STARK mit verwandten Systemen. Im zweiten Teil der Evaluierung zeigen wir, dass die transparente Wiederverwendung der materialisierten Zwischenergebnisse die Ausführungszeit der Programme signifikant verringern kann.Millions of location-aware devices, such as mobile phones, cars, and environmental sensors constantly report their positions often in combination with a timestamp to a server for different kinds of analyses. While the location information of the devices and reported events is represented as points and polygons, raster data is another type of spatial data, which is for example produced by cameras and sensors. This Big spatio-temporal Data needs to be processed on scalable platforms, such as Hadoop and Apache Spark, which, however, are unaware of, e.g., spatial neighborhood, what makes them practically impossible to use for this kind of data. The repeated executions of the programs during development and by different users result in long execution times and potentially high costs in rented clusters, which can be reduced by reusing commonly computed intermediate results. Within this thesis, we tackle the two challenges described above. First, we present the STARK framework for processing spatio-temporal vector and raster data on the Apache Spark stack. For operators, we identify several possible algorithms and study how they can benefit from the underlying platform's properties. We further investigate how indexes can be realized in the distributed and parallel architecture of Big Data processing engines and compare methods for data partitioning, which perform differently well with respect to data skew and data set size. Furthermore, an approach to reduce the amount of data to process at operator level is presented. In order to reduce the execution times, we introduce an approach to transparently recycle intermediate results of dataflow programs, based on operator costs. To compute the costs, we instrument the programs with profiling code to gather the execution time and result size of the operators. In the evaluation, we first compare the various implementation and configuration possibilities in STARK and identify scenarios when and how partitioning and indexing should be applied. We further compare STARK to related systems and show that we can achieve significantly better execution times, not only when exploiting existing partitioning information. In the second part of the evaluation, we show that with the transparent cost-based materialization and recycling of intermediate results, the execution times of programs can be reduced significantly

K-nearest neighbor search for fuzzy objects

The K-Nearest Neighbor search (kNN) problem has been investigated extensively in the past due to its broad range of applications. In this paper we study this problem in the context of fuzzy objects that have indeterministic boundaries. Fuzzy objects play an important role in many areas, such as biomedical image databases and GIS. Existing research on fuzzy objects mainly focuses on modelling basic fuzzy object types and operations, leaving the processing of more advanced queries such as kNN query untouched. In this paper, we propose two new kinds of kNN queries for fuzzy objects, Ad-hoc kNN query (AKNN) and Range kNN query (RKNN), to find the k nearest objects qualifying at a probability threshold or within a probability range. For efficient AKNN query processing, we optimize the basic best-first search algorithm by deriving more accurate approximations for the distance function between fuzzy objects and the query object. To improve the performance of RKNN search, effective pruning rules are developed to significantly reduce the search space and further speed up the candidate refinement process. The efficiency of our proposed algorithms as well as the optimization techniques are verified with an extensive set of experiments using both synthetic and real datasets

Providing Diversity in K-Nearest Neighbor Query Results

Given a point query Q in multi-dimensional space, K-Nearest Neighbor (KNN) queries return the K closest answers according to given distance metric in the database with respect to Q. In this scenario, it is possible that a majority of the answers may be very similar to some other, especially when the data has clusters. For a variety of applications, such homogeneous result sets may not add value to the user. In this paper, we consider the problem of providing diversity in the results of KNN queries, that is, to produce the closest result set such that each answer is sufficiently different from the rest. We first propose a user-tunable definition of diversity, and then present an algorithm, called MOTLEY, for producing a diverse result set as per this definition. Through a detailed experimental evaluation on real and synthetic data, we show that MOTLEY can produce diverse result sets by reading only a small fraction of the tuples in the database. Further, it imposes no additional overhead on the evaluation of traditional KNN queries, thereby providing a seamless interface between diversity and distance.Comment: 20 pages, 11 figure

Efficient processing of similarity queries with applications

Today, a myriad of data sources, from the Internet to business operations to scientific instruments, produce large and different types of data. Many application scenarios, e.g., marketing analysis, sensor networks, and medical and biological applications, call for identifying and processing similarities in big data. As a result, it is imperative to develop new similarity query processing approaches and systems that scale from low dimensional data to high dimensional data, from single machine to clusters of hundreds of machines, and from disk-based to memory-based processing. This dissertation introduces and studies several similarity-aware query operators, analyzes and optimizes their performance. The first contribution of this dissertation is an SQL-based Similarity Group-by operator (SGB, for short) that extends the semantics of the standard SQL Group-by operator to group data with similar but not necessarily equal values. We realize these SGB operators by extending the Standard SQL Group-by and introduce two new SGB operators for multi-dimensional data. We implement and test the new SGB operators and their algorithms inside an open-source centralized database server (PostgreSQL). In the second contribution of this dissertation, we study how to efficiently process Hamming-distance-based similarity queries (Hamming-distance select and Hamming-distance join) that are crucial to many applications. We introduce a new index, termed the HA-Index, that speeds up distance comparisons and eliminates redundancies when performing the two flavors of Hamming distance range queries (namely, the selects and joins). In the third and last contribution of this dissertation, we develop a system for similarity query processing and optimization in an in-memory and distributed setup for big spatial data. We propose a query scheduler and a distributed query optimizer that use a new cost model to optimize the cost of similarity query processing in this in-memory distributed setup. The scheduler and query optimizer generates query execution plans that minimize the effect of query skew. The query scheduler employs new spatial indexing techniques based on bloom filters to forward queries to the appropriate local sites. The proposed query processing and optimization techniques are prototyped inside Spark, a distributed main-memory computation system

Location- and keyword-based querying of geo-textual data: a survey

With the broad adoption of mobile devices, notably smartphones, keyword-based search for content has seen increasing use by mobile users, who are often interested in content related to their geographical location. We have also witnessed a proliferation of geo-textual content that encompasses both textual and geographical information. Examples include geo-tagged microblog posts, yellow pages, and web pages related to entities with physical locations. Over the past decade, substantial research has been conducted on integrating location into keyword-based querying of geo-textual content in settings where the underlying data is assumed to be either relatively static or is assumed to stream into a system that maintains a set of continuous queries. This paper offers a survey of both the research problems studied and the solutions proposed in these two settings. As such, it aims to offer the reader a first understanding of key concepts and techniques, and it serves as an “index” for researchers who are interested in exploring the concepts and techniques underlying proposed solutions to the querying of geo-textual data.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)Nanyang Technological UniversityThis research was supported in part by MOE Tier-2 Grant MOE2019-T2-2-181, MOE Tier-1 Grant RG114/19, an NTU ACE Grant, and the Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through the Industry Alignment Fund Industry Collaboration Projects Grant, and by the Innovation Fund Denmark centre, DIREC