221 research outputs found

    Modeling, Annotating, and Querying Geo-Semantic Data Warehouses

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    Optimizing Analytical Queries over Semantic Web Sources

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    Quarry: A user-centered big data integration platform

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    Obtaining valuable insights and actionable knowledge from data requires cross-analysis of domain data typically coming from various sources. Doing so, inevitably imposes burdensome processes of unifying different data formats, discovering integration paths, and all this given specific analytical needs of a data analyst. Along with large volumes of data, the variety of formats, data models, and semantics drastically contribute to the complexity of such processes. Although there have been many attempts to automate various processes along the Big Data pipeline, no unified platforms accessible by users without technical skills (like statisticians or business analysts) have been proposed. In this paper, we present a Big Data integration platform (Quarry) that uses hypergraph-based metadata to facilitate (and largely automate) the integration of domain data coming from a variety of sources, and provides an intuitive interface to assist end users both in: (1) data exploration with the goal of discovering potentially relevant analysis facets, and (2) consolidation and deployment of data flows which integrate the data, and prepare them for further analysis (descriptive or predictive), visualization, and/or publishing. We validate Quarry’s functionalities with the use case of World Health Organization (WHO) epidemiologists and data analysts in their fight against Neglected Tropical Diseases (NTDs).This work is partially supported by GENESIS project, funded by the Spanish Ministerio de Ciencia, Innovación y Universidades under project TIN2016-79269-R.Peer ReviewedPostprint (author's final draft

    Aspects of Semantic ETL

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    Aspects of semantic ETL

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    Tesi en modalitat de cotutela: Universitat Politècnica de Catalunya i Aalborg UniversitetBusiness Intelligence tools support making better business decisions by analyzing available organizational data. Data Warehouses (DWs), typically structured with the Multidimensional (MD) model, are used to store data from different internal and external sources processed using Extract-Transformation-Load (ETL) processes. On-Line analytical Processing (OLAP) queries are applied on DWs to derive important business-critical knowledge. DW and OLAP technologies perform efficiently when they are applied on data that are static in nature and well organized in structure. Nowadays, Semantic Web technologies and the Linked Data principles inspire organizations to publish their semantic data, which allow machines to understand the meaning of data, using the Resource Description Framework (RDF) model. In addition to traditional (non-semantic) data sources, the incorporation of semantic data sources into a DW raises the additional challenges of schema derivation, semantic heterogeneity, and schema and data management model over traditional ETL tools. Furthermore, most SW data provided by business, academic and governmental organizations include facts and figures, which raise new requirements for BI tools to enable OLAP-like analyses over those semantic (RDF) data. In this thesis, we 1) propose a layer-based ETL framework for handling diverse semantic and non-semantic data sources by addressing the challenges mentioned above, 2) propose a set of high-level ETL constructs for processing semantic data, 3) implement appropriate environments (both programmable and GUI) to facilitate ETL processes and evaluate the proposed solutions. Our ETL framework is a semantic ETL framework because it integrates data semantically. We propose SETL, a unified framework for semantic ETL. The framework is divided into three layers: the Definition Layer, ETL Layer, and Data Warehouse Layer. In the Definition Layer, the semantic DW (SDW) schema, sources, and the mappings among the sources and the target are defined. In the ETL Layer, ETL processes to populate the SDW from sources are designed. The Data Warehouse Layer manages the storage of transformed semantic data. The framework supports the inclusion of semantic (RDF) data in DWs in addition to relational data. It allows users to define an ontology of a DW and annotate it with MD constructs (such as dimensions, cubes, levels, etc.) using the Data Cube for OLAP (QB4OLAP) vocabulary. It supports traditional transformation operations and provides a method to generate semantic data from the source data according to the semantics encoded in the ontology. It also provides a method to connect internal SDW data with external knowledge bases. On top of SETL, we propose SETLCONSTUCT where we define a set of high-level ETL tasks/operations to process semantic data sources. We divide the integration process into two layers: the Definition Layer and Execution Layer. The Definition Layer includes two tasks that allow DW designers to define target (SDW) schemas and the mappings between (intermediate) sources and the (intermediate) target. To create mappings among the sources and target constructs, we provide a mapping vocabulary called S2TMAP. Different from other ETL tools, we propose a new paradigm: we characterize the ETL flow transformations at the Definition Layer instead of independently within each ETL operation (in the Execution Layer). This way, the designer has an overall view of the process, which generates metadata (the mapping file) that the ETL operators will read and parametrize themselves with automatically. In the Execution Layer, we propose a set of high-level ETL operations to process semantic data sources. Finally, we develop a GUI-based semantic BI system SETLBI to define, process, integrate, and query semantic and non-semantic data. In addition to the Definition Layer and the ETL Layer, SETLBI has the OLAP Layer, which provides an interactive interface to enable OLAP analysis over the semantic DWLes eines d’Intel·ligència Empresarial (BI), conegudes en anglès com Business Intelligence, donen suport a la millora de la presa de decisions empresarials mitjançant l’anàlisi de les dades de l’organització disponibles. Els magatzems de dades, o data warehouse, (DWs), típicament estructurats seguint el model Multidimensional (MD), s’utilitzen per emmagatzemar dades de diferents fonts, tant internes com externes, processades mitjançant processos Extract- Transformation-Load (ETL). Les consultes de processament analític en línia (OLAP) s’apliquen als DW per extraure coneixement crític en l’àmbit empresarial. Els DW i les tecnologies OLAP funcionen de manera eficient quan s’apliquen sobre dades de natura estàtica i ben estructurades. Avui en dia, les tecnologies de la Web Semàntica (SW) i els principis Linked Data (LD) inspiren les organitzacions per publicar les seves dades en formats semàntics, que permeten que les màquines entenguin el significat de les dades, mitjançant el llenguatge de descripció de recursos (RDF). Una de les raons per les quals les dades semàntiques han tingut tant d’èxit és que es poden gestionar i fer que estiguin disponibles per tercers amb poc esforç, i no depenen d’esquemes de dades sofisticats. A més de les fonts de dades tradicionals (no semàntiques), la incorporació de fonts de dades semàntiques en un DW planteja reptes addicionals tals com derivar-hi esquema, l’heterogeneïtat semàntica i la representació de l’esquema i les dades a través d’eines d’ETL. A més, la majoria de dades SW proporcionades per empreses, organitzacions acadèmiques o governamentals inclouen fets i figures que representen nous reptes per les eines de BI per tal d’habilitar l’anàlisi OLAP sobre dades semàntiques (RDF). En aquesta tesi, 1) proposem un marc ETL basat en capes per a la gestió de diverses fonts de dades semàntiques i no semàntiques i adreçant els reptes esmentats anteriorment, 2) proposem un conjunt d’operacions ETL per processar dades semàntiques, i 3) la creació d’entorns apropiats de desenvolupament (programàtics i GUIs) per facilitar la creació i gestió de DW i processos ETL semàntics, així com avaluar les solucions proposades. El nostre marc ETL és un marc ETL semàntic perquè Es capaç de considerar e integrar dades de forma semàntica. Els següents paràgrafs elaboren sobre aquests contribucions. Proposem SETL, un marc unificat per a ETL semàntic. El marc es divideix en tres capes: la capa de definició, la capa ETL i la capa DW. A la capa de definició, es defineixen l’esquema del DW semàntic (SDW), les fonts i els mappings entre les fonts i l’esquema del DW. A la capa ETL, es dissenyen processos ETL per popular el SDW a partir de fonts. A la capa DW, es gestiona l’emmagatzematge de les dades semàntiques transformades. El nostre marc dóna suport a la inclusió de dades semàntiques (RDF) en DWs, a més de dades relacionals. Així, permet als usuaris definir una ontologia d’un DW i anotar-la amb construccions MD (com ara dimensions, cubs, nivells, etc.) utilitzant el vocabulari Data Cube for OLAP (QB4OLAP). També admet operacions de transformació tradicionals i proporciona un mètode per generar semàntica de les dades d’origen segons la semàntica codificada al document ontologia. També proporciona un mètode per connectar l’SDW amb bases de coneixement externes. Per tant, crea una base de coneixement, composta per un ontologia i les seves instàncies, on les dades estan connectades semànticament amb altres dades externes / internes. Per fer-ho, desenvolupem un mètode programàtic, basat en Python, d’alt nivell, per realitzar les tasques esmentades anteriorment. S’ha portat a terme un experiment complet d’avaluació comparant SETL amb una solució elaborada amb eines tradicional (que requereixen molta més codificació). Com a cas d’ús, hem emprat el Danish Agricultural dataset, i els resultats mostren que SETL proporciona un millor rendiment, millora la productivitat del programador i la qualitat de la base de coneixement. La comparació entre SETL i Pentaho Data Integration (PDI) mostra que SETL és un 13,5% més ràpid que PDI. A més de ser més ràpid que PDI, tracta les dades semàntiques com a ciutadans de primera classe, mentre que PDI no conté operadors específics per a dades semàntiques. A sobre de SETL, proposem SETLCONSTUCT on definim un conjunt de tasques d’alt nivell / operacions ETL per processar fonts de dades semàntiques i orientades a encapsular i facilitar la creació de l’ETL semàntic. Dividim el procés d’integració en dues capes: la capa de definició i la capa d’execució. La capa de definició inclou dues tasques que permeten definir als dissenyadors de DW esquemes destí (SDW) i mappings entre fonts (o resultats intermedis) i l’SDW (potencialment, altres resultats intermedis). Per crear mappings entre les fonts i el SDW, proporcionem un vocabulari de mapping anomenat Source-To-Target Mapping (S2TMAP). A diferència d’altres eines ETL, proposem un nou paradigma: les transformacions del flux ETL es caracteritzen a la capa de definició, i no de forma independent dins de cada operació ETL (a la capa d’execució). Aquest nou paradigma permet al dissenyador tenir una visió global del procés, que genera metadades (el fitxer de mapping) que els operadors ETL individuals llegiran i es parametritzaran automàticament. A la capa d’execució proposem un conjunt d’operacions ETL d’alt nivell per processar fonts de dades semàntiques. A més de la neteja, la unió i la transformació per dades semàntiques, proposem operacions per generar semàntica multidimensional i actualitzar el SDW per reflectir els canvis en les fonts. A més, ampliem SETLCONSTRUCT per permetre la generació automàtica de flux d’execució ETL (l’anomenem SETLAUTO). Finalment, proporcionem una àmplia avaluació per comparar la productivitat, el temps de desenvolupament i el rendiment de SETLCONSTRUCT i SETLAUTO amb el marc anterior SETL. L’avaluació demostra que SETLCONSTRUCT millora considerablement sobre SETL en termes de productivitat, temps de desenvolupament i rendiment. L’avaluació mostra que 1) SETLCONSTRUCT utilitza un 92% menys de caràcters mecanografiats (NOTC) que SETL, i SETLAUTO redueix encara més el nombre de conceptes usats (NOUC) un altre 25%; 2) utilitzant SETLCONSTRUCT, el temps de desenvolupament es redueix gairebé a la meitat en comparació amb SETL, i es redueix un altre 27 % mitjançant SETLAUTO; 3) SETLCONSTRUCT es escalable i té un rendiment similar en comparació amb SETL. Finalment, desenvolupem un sistema de BI semàntic basat en GUI SETLBI per definir, processar, integrar i consultar dades semàntiques i no semàntiques. A més de la capa de definició i de la capa ETL, SETLBI té una capa OLAP, que proporciona una interfície interactiva per permetre l’anàlisi OLAP d’autoservei sobre el DW semàntic. Cada capa està composada per un conjunt d’operacions / tasques. Per formalitzar les connexions intra i inter-capes dels components de cada capa, emprem una ontologia. La capa ETL amplia l’execució de la capa de SETLCONSTUCT afegint operacions per processar fonts de dades no semàntiques. Per últim, demostrem el sistema final mitjançant el cens de la població de Bangladesh (2011). La solució final d’aquesta tesi és l’eina SETLBI . SETLBI facilita (1) als dissenyadors del DW amb pocs / sense coneixements de SW, integrar semànticament les dades (semàntiques o no) i analitzar-les emprant OLAP, i (2) als usuaris de la SW els permet definir vistes sobre dades semàntiques, integrar-les amb fonts no semàntiques, i visualitzar-les segons el model MD i fer anàlisi OLAP. A més, els usuaris SW poden enriquir l’esquema SDW generat amb construccions RDFS / OWL. Prenent aquest marc com a punt de partida, els investigadors poden emprar-lo per a crear SDWs de forma interactiva i automàtica. Aquest projecte crea un pont entre les tecnologies BI i SW, i obre la porta a altres oportunitats de recerca com desenvolupar tècniques de DW i ETL comprensibles per les màquines.(Danskere) Business Intelligence (BI) værktøjer understøtter at tage bedre forretningsbeslutninger, ved at analysere tilgængelige organisatoriske data. Data Warehouses (DWs), typisk konstrueret med den Multidimensionelle (MD) model, bruges til at lagre data fra forskellige interne og eksterne kilder, der behandles ved hjælp af Extract-Transformation-Load (ETL) processer. On-Line Analytical Processing (OLAP) forespørgsler anvendes på DWs for at udlede vigtig forretningskritisk viden. DW og OLAP-teknologier fungerer effektivt, når de anvendes på data, som er statiske af natur og velorganiseret i struktur. I dag inspirerer Semantic Web (SW) teknologier og Linked Data (LD) principper organisationer til at offentliggøre deres semantiske data, som tillader maskiner at forstå betydningen af denne, ved hjælp af Resource Description Framework (RDF) modellen. En af grundene til, at semantiske data er blevet succesfuldt, er at styringen og udgivelsen af af dataene er nemt, og ikke er afhængigt af et sofistikeret skema. Ud over problemer ved overførslen af traditionelle (ikke-semantiske) databaser til DWs, opstår yderligere udfordringer ved overførslen af semantiske databaser, såsom skema nedarvning, semantisk heterogenitet samt skemaet for data repræsentation over traditionelle ETL værktøjer. På den anden side udgør en stor del af den semantiske data der bliver offentliggjort af virksomheder, akademikere samt regeringer, af figurer og fakta, der igen giver nye problemstillinger og krav til BI værktøjer, for at gøre OLAP lignende analyser over de semantiske data mulige. I denne afhandling gør vi følgende: 1) foreslår et lag-baseret ETL framework til at håndterer multiple semantiske og ikke-semantiske datakilder, ved at svare på udfordringerne nævnt herover, 2) foreslår en mængde af ETL operationer til at behandle semantisk data, 3) implementerer passende miljøer (både programmerbare samt grafiske brugergrænseflader), for at lette ETL processer og evaluere den foreslåede løsning. Vores ETL framework er et semantisk ETL framework, fordi det integrerer data semantisk. Den følgende sektion forklarer vores bidrag. Vi foreslår SETL, et samlet framework for semantisk ETL. Frameworket er splittet i tre lag: et definitions-lag, et ETL-lag, og et DW-lag. Det semanvii tiske DW (SWD) skema, datakilder, samt sammenhængen mellem datakilder og deres mål, er defineret i definitions-laget. I ETL-laget designes ETLprocesser til at udfylde SDW fra datakilderne. DW-laget administrerer lagring af transformerede semantiske data. Frameworket understøtter inkluderingen af semantiske (RDF) data i DWs ud over relationelle data. Det giver brugerne mulighed for at definere en ontologi for et DW og annotere med MD-konstruktioner (såsom dimensioner, kuber, niveauer osv.) ved hjælp af Data Cube til OLAP (QB4OLAP) ordforrådet. Det understøtter traditionelle transformations operationer, og giver en metode til at generere semantiske data fra de oprindelige data, i henhold til semantikken indkodet i ontologien. Det muliggør også en metode til at forbinde interne SDW data med eksterne vidensbaser. Herved skaber det en vidensbase, der er sammensat af en ontologi og dets instanser, hvor data er semantisk forbundet med andre eksterne / interne data. Vi udvikler et høj niveau Python-baseret programmerbart framework for at udføre de ovennævnte opgaver. En omfattende eksperimentel evaluering, der sammenligner SETL med en traditionel løsning (hvilket krævede meget manuel kodning), om brugen af danske landbrugsog forretnings datasæt, viser at SETL præsterer bedre, programmør produktivitet og vidensbase kvalitet. Sammenligningen mellem SETL og Pentaho Data Integration (PDI) ved behandling af en semantisk kilde viser, at SETL er 13,5% hurtigere end PDI. Udover SETL, foreslår vi SETLCONSTRUCT hvor vi definerer et sæt ETLoperationer på højt niveau til behandling af semantiske datakilder. Vi deler integrationsprocessen i to lag: Definitions-lag og eksekverings-lag. Definitionslaget indeholder to opgaver, der giver DW designere muligheden for at definere (SDW) skemaer, og kortlægningerne mellem kilder og målet. For at oprette kortlægning mellem kilderne og målene, leverer vi et kortlægnings ordforråd kaldet Source-to-Target Mapping (S2TMAP). Forskelligt fra andre ETL-værktøjer foreslår vi et nyt paradigme: vi karakteriserer ETLflowtransformationerne i definitions-laget i stedet for uafhængigt inden for hver ETL-operation (i eksekverings-laget). På denne måde har designeren et overblik over processen, som genererer metadata (kortlægningsfilen), som ETL operatørerne vil læse og parametrisere automatisk. I eksekverings-laget foreslår vi en mængde høj niveau ETL-operationer til at behandle semantiske datakilder. Udover rensning, sammenføjning og datatypebaseret transformationer af semantiske data, foreslår vi operationer til at generere multidimensionel semantik på data-niveau og operationer til at opdatere et SDW for at afspejle ændringer i kilde-dataen. Derudover udvider vi SETLCONSTRUCT for at muliggøre automatisk ETL-eksekveringsstrømgenerering (vi kalder det SETLAUTO). Endelig leverer vi en omfattende evaluering for at sammenligne produktivitet, udviklingstid og ydeevne for scon og SETLAUTO med den tidligere ramme SETL. Evalueringen viser, at SETLCONSTRUCT forbedres markant i forhold til SETL med hensyn til produktivitet, udviklingstid og ydeevne. Evalueringen viser, at 1) SETLCONSTRUCT bruger 92% færre antal indtastede tegn (NOTC) end SETL, og SETLAUTO reducerer antallet af brugte begreber (NOUC) yderligere med 25%; 2) ved at bruge SETLCONSTRUCT, er udviklingstiden næsten halveret sammenlignet med SETL, og skæres med yderligere 27% ved hjælp af SETLAUTO; 3) SETLCONSTRUCT er skalerbar og har lignende ydelse sammenlignet med SETL. Til slut udvikler vi et GUI-baseret semantisk BI system SETLBI for at definere, processere, integrere og lave forespørgsler på semantiske og ikkesemantiske data. Ud over definitions-laget og ETL-laget, har SETLBI et OLAP-lag, som giver en interaktiv grænseflade for at muliggøre selvbetjenings OLAP analyser over det semantiske DW. Hvert lag er sammensat af en mængde operationer/opgaver. Vi udarbejder en ontologi til at formalisere intra-og ekstra-lags forbindelserne mellem komponenterne og lagene. ETLlaget udvider eksekverings-laget af SETLCONSTUCT ved at tilføje operationer til at behandle ikke-semantiske datakilder. Vi demonstrerer systemet ved hjælp af Bangladesh population census 2011 datasættet. Sammenfatningen af denne afhandling er BI-værktøjet SETLBI . SETLBI fremmer (1) DW-designere med ringe / ingen SW-viden til semantisk at integrere semantiske og / eller ikke-semantiske data og analysere det i OLAP stil, og (2) SW brugere med grundlæggende MD-baggrund til at definere MDvisninger over semantiske data, der aktiverer OLAP-lignende analyse. Derudover kan SW-brugere berige det genererede SDW-skema med RDFS / OWLkonstruktioner. Med udgangspunkt i frameworket som et grundlag kan forskere sigte mod at udvikle yderligere interaktive og automatiske integrationsrammer for SDW. Dette projekt bygger bro mellem de traditionelle BIteknologier og SW-teknologier, som igen vil åbne døren for yderligere forskningsmuligheder som at udvikle maskinforståelige ETL og lagerteknikker.Postprint (published version

    Concepts and Techniques for Flexible and Effective Music Data Management

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    LLM-FuncMapper: Function Identification for Interpreting Complex Clauses in Building Codes via LLM

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    As a vital stage of automated rule checking (ARC), rule interpretation of regulatory texts requires considerable effort. However, interpreting regulatory clauses with implicit properties or complex computational logic is still challenging due to the lack of domain knowledge and limited expressibility of conventional logic representations. Thus, LLM-FuncMapper, an approach to identifying predefined functions needed to interpret various regulatory clauses based on the large language model (LLM), is proposed. First, by systematically analysis of building codes, a series of atomic functions are defined to capture shared computational logics of implicit properties and complex constraints, creating a database of common blocks for interpreting regulatory clauses. Then, a prompt template with the chain of thought is developed and further enhanced with a classification-based tuning strategy, to enable common LLMs for effective function identification. Finally, the proposed approach is validated with statistical analysis, experiments, and proof of concept. Statistical analysis reveals a long-tail distribution and high expressibility of the developed function database, with which almost 100% of computer-processible clauses can be interpreted and represented as computer-executable codes. Experiments show that LLM-FuncMapper achieve promising results in identifying relevant predefined functions for rule interpretation. Further proof of concept in automated rule interpretation also demonstrates the possibility of LLM-FuncMapper in interpreting complex regulatory clauses. To the best of our knowledge, this study is the first attempt to introduce LLM for understanding and interpreting complex regulatory clauses, which may shed light on further adoption of LLM in the construction domain

    Qualitative Spatial Reasoning with Holed Regions

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    The intricacies of real-world and constructed spatial entities call for versatile spatial data types to model complex spatial objects, often characterized by the presence of holes. To date, however, relations of simple, hole-free regions have been the prevailing approaches for spatial qualitative reasoning. Even though such relations may be applied to holed regions, they do not take into consideration the consequences of the existence of the holes, limiting the ability to query and compare more complex spatial configurations. To overcome such limitations, this thesis develops a formal framework for spatial reasoning with topological relations over two-dimensional holed regions, called the Holed Regions Model (HRM), and a similarity evaluation method for comparing relations featuring a multi-holed region, called the Frequency Distribution Method (FDM). The HRM comprises a set of 23 relations between a hole-free and a single-holed region, a set of 152 relations between two single-holed regions, as well as the composition inferences enabled from both sets of relations. The inference results reveal that the fine-grained topological relations over holed regions provide more refined composition results in over 50% of the cases when compared with the results of hole-free regions relations. The HRM also accommodates the relations between a hole-free region and a multi-holed region. Each such relation is called a multi-element relation, as it can be deconstructed into a number of elements—relations between a hole-free and a singleholed region—that is equal to the number of holes, regarding each hole as if it were the only one. FDM facilitates the similarity assessment among multi-element relations. The similarity is evaluated by comparing the frequency summaries of the single-holed region relations. The multi-holed regions of the relations under comparison may differ in the number of holes. In order to assess the similarity of such relations, one multi-holed region is considered as the result of dropping from or adding holes to the other region. Therefore, the effect that two concurrent changes have on the similarity of the relations is evaluated. The first is the change in the topological relation between the regions, and the second is the change in a region’s topology brought upon by elimination or addition of holes. The results from the similarity evaluations examined in this thesis show that the topological placement of the holes in relation to the hole-free region influences relation similarity as much as the relation between the hole-free region and the host of the holes. When the relations under comparison have fewer characteristics in common, the placement of the holes is the determining factor for the similarity rankings among relations. The distilled and more correct composition and similarity evaluation results enabled by the relations over holed regions indicate that spatial reasoning over such regions differs from the prevailing reasoning over hole-free regions. Insights from such results are expected to contribute to the design of future geographic information systems that more adequately process complex spatial phenomena, and are better equipped for advanced database query answering

    Qualitative Spatial Reasoning with Holed Regions

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    The intricacies of real-world and constructed spatial entities call for versatile spatial data types to model complex spatial objects, often characterized by the presence of holes. To date, however, relations of simple, hole-free regions have been the prevailing approaches for spatial qualitative reasoning. Even though such relations may be applied to holed regions, they do not take into consideration the consequences of the existence of the holes, limiting the ability to query and compare more complex spatial configurations. To overcome such limitations, this thesis develops a formal framework for spatial reasoning with topological relations over two-dimensional holed regions, called the Holed Regions Model (HRM), and a similarity evaluation method for comparing relations featuring a multi-holed region, called the Frequency Distribution Method (FDM). The HRM comprises a set of 23 relations between a hole-free and a single-holed region, a set of 152 relations between two single-holed regions, as well as the composition inferences enabled from both sets of relations. The inference results reveal that the fine-grained topological relations over holed regions provide more refined composition results in over 50% of the cases when compared with the results of hole-free regions relations. The HRM also accommodates the relations between a hole-free region and a multi-holed region. Each such relation is called a multi-element relation, as it can be deconstructed into a number of elements—relations between a hole-free and a singleholed region—that is equal to the number of holes, regarding each hole as if it were the only one. FDM facilitates the similarity assessment among multi-element relations. The similarity is evaluated by comparing the frequency summaries of the single-holed region relations. The multi-holed regions of the relations under comparison may differ in the number of holes. In order to assess the similarity of such relations, one multi-holed region is considered as the result of dropping from or adding holes to the other region. Therefore, the effect that two concurrent changes have on the similarity of the relations is evaluated. The first is the change in the topological relation between the regions, and the second is the change in a region’s topology brought upon by elimination or addition of holes. The results from the similarity evaluations examined in this thesis show that the topological placement of the holes in relation to the hole-free region influences relation similarity as much as the relation between the hole-free region and the host of the holes. When the relations under comparison have fewer characteristics in common, the placement of the holes is the determining factor for the similarity rankings among relations. The distilled and more correct composition and similarity evaluation results enabled by the relations over holed regions indicate that spatial reasoning over such regions differs from the prevailing reasoning over hole-free regions. Insights from such results are expected to contribute to the design of future geographic information systems that more adequately process complex spatial phenomena, and are better equipped for advanced database query answering

    Structure and representation of ecological data to support knowledge discovery: A case study with bioacoustic data

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    Bird communities have long been surveyed as key indicators of ecosystem health and biodiversity. Adoption of Autonomous Recording Units (ARUs) to perform avian surveys has shifted the burden of species recognition from “birders” in the field, to “listeners” who review the ARU recordings at a later time. The number of recordings ARUs can produce has created a need to process large amounts of data. Although much research is devoted to fully automating the recognition process, expert humans are still required when entire bird communities must be identified. A framework for a Decision Support System (DSS) is presented which would assist listeners by suggesting likely species. A unique feature of the DSS is the consideration of the recording “context” of time, location and habitat as well as the bioacoustic features to match unknown vocalizations with reference species. In this thesis a data warehouse was built for an existing set of bioacoustic research data as a first–step to creating the DSS. The data set was from ARU deployments in the Lower Athabasca Region of Alberta, Canada. The Knowledge Discovery in Databases (KDD) and Dimensional Design Process protocols were used as guides to build a Kimball–style data warehouse. Data housed in the data warehouse included field data, data derived from GIS analysis, fuzzy logic memberships and symbolic representation of bioacoustic recording using the Piecewise Aggregate Approximation and Symbolic Aggregate approXimation (PAA/SAX). Examples of how missing and erroneous data were detected and processed are given. The sources of uncertainty inherent in ecological data are discussed and fuzzy logic is demonstrated as a soft–computing technique to accommodate this data. Data warehouses are commonly used for business applications but are very applicable for ecological data. As most instructions on building data warehouse are for business data, this thesis is offered as an example for ecologists interested in moving their data to a data warehouse. This thesis presents a case–study of how a data warehouse can be constructed for existing ecological data, whether as part of a DSS or a tool for viewing research data.Symbolic aggregate approximationBioacousticsDecision support systemData warehouseFuzzy logicBirdsAutonomous recording unitsPiecewise aggregate approximatio
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