R-Forest for Approximate Nearest Neighbor Queries in High Dimensional Space

Searching high dimensional space has been a challenge and an area of intense research for many years. The dimensionality curse has rendered most existing index methods all but useless causing people to research other techniques. In my dissertation I will try to resurrect one of the best known index structures, R-Tree, which most have given up on as a viable method of answering high dimensional queries. I have pointed out the various advantages of R-Tree as a method for answering approximate nearest neighbor queries, and the advantages of locality sensitive hashing and locality sensitive B-Tree, which are the most successful methods today. I started by looking at improving the maintenance of R-Tree by the use of bulk loading and insertion. I proposed and implemented a new method that bulk loads the index which was an improvement of standard method. I then turned my attention to nearest neighbor queries, which is a much more challenging problem especially in high dimensional space. Initially I developed a set of heuristics, easily implemented in R-Tree, which improved the efficiency of high dimensional approximate nearest neighbor queries. To further refine my method I took another approach, by developing a new model, known as R-Forest, which takes advantage of space partitioning while still using R-Tree as its index structure. With this new approach I was able to implement new heuristics and can show that R-Forest, comprised of a set of R-Trees, is a viable solution tohigh dimensional approximate nearest neighbor queries when compared to established methods

空間Webデータにおけるm-最近接キーワード検索問題のトップダウン解法に関する研究

This thesis addresses the problem of m-closest keywords queries (mCK queries) over spatial web objects that contain descriptive texts and spatial information. The mCK query is a problem to find the optimal set of records in the sense that they are the spatially-closest records that satisfy m user-given keywords in their texts. The mCK query can be widely used in various applications to find the place of user’s interest.　Generally, top-down search techniques using tree-style data structures are appropriate for finding optimal results of queries over spatial datasets. Thus in order to solve the mCK query problem, a previous study of NUS group assumed a specialized R*-tree (called bR*-tree) to store all records and proposed a top-down approach which uses an Apriori-based node-set enumeration in top-down process. However this assumption of prepared bR*-tree is not applicable to practical spatial web datasets, and the pruning ability of Apriori-based enumeration is highly dependent on the data distribution.　In this thesis, we do not expect any prepared data-partitioning, but assume that we create a grid partitioning from necessary data only when an mCK query is given. Under this assumption, we propose a new search strategy termed Diameter Candidate Check (DCC), which can find a smaller node-set at an earlier stage of search so that it can reduce search space more efficiently. According to DCC search strategy, we firstly employ an implementation of DCC strategy in a nested loop search algorithm (called DCC-NL). Next, we improve the DCC-NL in a recursive way (called RDCC). RDCC can afford a more reasonable priority order of node-set enumeration. We also uses a tight lower bound to improve pruning ability in RDCC.　RDCC performs well in a wide variey of data distributions, but it has still deficiency when one data-point has many query keywords and numerous node-sets are generated. Hence in order to avoid the generation of node-sets which is an unstable factor of search efficiency, we propose another different top-down search approach called Pairwise Expansion. Finally, we discuss some optimization techniques to enhance Pairwise Expansion approach. We first discuss the index structure in the Pairwise Expansion approach, and try to use an on-the-fly kd-tree to reduce building cost in the query process. Also a new lower bound and an upper bound are employed for more powerful pruning in Pairwise Expansion.　We evaluate these approaches by using both real datasets and synthetic datasets for different data distributions, including 1.6 million of Flickr photo data. The result shows that DCC strategy can provide more stable search performance than the Apriori-based approach. And the Pairwise Expansion approach enhanced with lower/upper bounds, has more advantages over those algorithms having node-set generation, and is applicable for real spatial web data.電気通信大学201

Searching and mining in enriched geo-spatial data

The emergence of new data collection mechanisms in geo-spatial applications paired with a heightened tendency of users to volunteer information provides an ever-increasing flow of data of high volume, complex nature, and often associated with inherent uncertainty. Such mechanisms include crowdsourcing, automated knowledge inference, tracking, and social media data repositories. Such data bearing additional information from multiple sources like probability distributions, text or numerical attributes, social context, or multimedia content can be called multi-enriched. Searching and mining this abundance of information holds many challenges, if all of the data's potential is to be released. This thesis addresses several major issues arising in that field, namely path queries using multi-enriched data, trend mining in social media data, and handling uncertainty in geo-spatial data. In all cases, the developed methods have made significant contributions and have appeared in or were accepted into various renowned international peer-reviewed venues. A common use of geo-spatial data is path queries in road networks where traditional methods optimise results based on absolute and ofttimes singular metrics, i.e., finding the shortest paths based on distance or the best trade-off between distance and travel time. Integrating additional aspects like qualitative or social data by enriching the data model with knowledge derived from sources as mentioned above allows for queries that can be issued to fit a broader scope of needs or preferences. This thesis presents two implementations of incorporating multi-enriched data into road networks. In one case, a range of qualitative data sources is evaluated to gain knowledge about user preferences which is subsequently matched with locations represented in a road network and integrated into its components. Several methods are presented for highly customisable path queries that incorporate a wide spectrum of data. In a second case, a framework is described for resource distribution with reappearance in road networks to serve one or more clients, resulting in paths that provide maximum gain based on a probabilistic evaluation of available resources. Applications for this include finding parking spots. Social media trends are an emerging research area giving insight in user sentiment and important topics. Such trends consist of bursts of messages concerning a certain topic within a time frame, significantly deviating from the average appearance frequency of the same topic. By investigating the dissemination of such trends in space and time, this thesis presents methods to classify trend archetypes to predict future dissemination of a trend. Processing and querying uncertain data is particularly demanding given the additional knowledge required to yield results with probabilistic guarantees. Since such knowledge is not always available and queries are not easily scaled to larger datasets due to the #P-complete nature of the problem, many existing approaches reduce the data to a deterministic representation of its underlying model to eliminate uncertainty. However, data uncertainty can also provide valuable insight into the nature of the data that cannot be represented in a deterministic manner. This thesis presents techniques for clustering uncertain data as well as query processing, that take the additional information from uncertainty models into account while preserving scalability using a sampling-based approach, while previous approaches could only provide one of the two. The given solutions enable the application of various existing clustering techniques or query types to a framework that manages the uncertainty.Das Erscheinen neuer Methoden zur Datenerhebung in räumlichen Applikationen gepaart mit einer erhöhten Bereitschaft der Nutzer, Daten über sich preiszugeben, generiert einen stetig steigenden Fluss von Daten in großer Menge, komplexer Natur, und oft gepaart mit inhärenter Unsicherheit. Beispiele für solche Mechanismen sind Crowdsourcing, automatisierte Wissensinferenz, Tracking, und Daten aus sozialen Medien. Derartige Daten, angereichert mit mit zusätzlichen Informationen aus verschiedenen Quellen wie Wahrscheinlichkeitsverteilungen, Text- oder numerische Attribute, sozialem Kontext, oder Multimediainhalten, werden als multi-enriched bezeichnet. Suche und Datamining in dieser weiten Datenmenge hält viele Herausforderungen bereit, wenn das gesamte Potenzial der Daten genutzt werden soll. Diese Arbeit geht auf mehrere große Fragestellungen in diesem Feld ein, insbesondere Pfadanfragen in multi-enriched Daten, Trend-mining in Daten aus sozialen Netzwerken, und die Beherrschung von Unsicherheit in räumlichen Daten. In all diesen Fällen haben die entwickelten Methoden signifikante Forschungsbeiträge geleistet und wurden veröffentlicht oder angenommen zu diversen renommierten internationalen, von Experten begutachteten Konferenzen und Journals. Ein gängiges Anwendungsgebiet räumlicher Daten sind Pfadanfragen in Straßennetzwerken, wo traditionelle Methoden die Resultate anhand absoluter und oft auch singulärer Maße optimieren, d.h., der kürzeste Pfad in Bezug auf die Distanz oder der beste Kompromiss zwischen Distanz und Reisezeit. Durch die Integration zusätzlicher Aspekte wie qualitativer Daten oder Daten aus sozialen Netzwerken als Anreicherung des Datenmodells mit aus diesen Quellen abgeleitetem Wissen werden Anfragen möglich, die ein breiteres Spektrum an Anforderungen oder Präferenzen erfüllen. Diese Arbeit präsentiert zwei Ansätze, solche multi-enriched Daten in Straßennetze einzufügen. Zum einen wird eine Reihe qualitativer Datenquellen ausgewertet, um Wissen über Nutzerpräferenzen zu generieren, welches darauf mit Örtlichkeiten im Straßennetz abgeglichen und in das Netz integriert wird. Diverse Methoden werden präsentiert, die stark personalisierbare Pfadanfragen ermöglichen, die ein weites Spektrum an Daten mit einbeziehen. Im zweiten Fall wird ein Framework präsentiert, das eine Ressourcenverteilung im Straßennetzwerk modelliert, bei der einmal verbrauchte Ressourcen erneut auftauchen können. Resultierende Pfade ergeben einen maximalen Ertrag basieren auf einer probabilistischen Evaluation der verfügbaren Ressourcen. Eine Anwendung ist die Suche nach Parkplätzen. Trends in sozialen Medien sind ein entstehendes Forscchungsgebiet, das Einblicke in Benutzerverhalten und wichtige Themen zulässt. Solche Trends bestehen aus großen Mengen an Nachrichten zu einem bestimmten Thema innerhalb eines Zeitfensters, so dass die Auftrittsfrequenz signifikant über den durchschnittlichen Level liegt. Durch die Untersuchung der Fortpflanzung solcher Trends in Raum und Zeit präsentiert diese Arbeit Methoden, um Trends nach Archetypen zu klassifizieren und ihren zukünftigen Weg vorherzusagen. Die Anfragebearbeitung und Datamining in unsicheren Daten ist besonders herausfordernd, insbesondere im Hinblick auf das notwendige Zusatzwissen, um Resultate mit probabilistischen Garantien zu erzielen. Solches Wissen ist nicht immer verfügbar und Anfragen lassen sich aufgrund der \P-Vollständigkeit des Problems nicht ohne Weiteres auf größere Datensätze skalieren. Dennoch kann Datenunsicherheit wertvollen Einblick in die Struktur der Daten liefern, der mit deterministischen Methoden nicht erreichbar wäre. Diese Arbeit präsentiert Techniken zum Clustering unsicherer Daten sowie zur Anfragebearbeitung, die die Zusatzinformation aus dem Unsicherheitsmodell in Betracht ziehen, jedoch gleichzeitig die Skalierbarkeit des Ansatzes auf große Datenmengen sicherstellen