Compact semantic representations of observational data

Das Konzept des Internet der Dinge (IoT) ist in mehreren Bereichen weit verbreitet, damit Geräte miteinander interagieren und bestimmte Aufgaben erfüllen können. IoT-Geräte umfassen verschiedene Konzepte, z.B. Sensoren, Programme, Computer und Aktoren. IoT-Geräte beobachten ihre Umgebung, um Informationen zu sammeln und miteinander zu kommunizieren, um gemeinsame Aufgaben zu erfüllen. Diese Vorrichtungen erzeugen kontinuierlich Beobachtungsdatenströme, die zu historischen Daten werden, wenn diese Beobachtungen gespeichert werden. Durch die Zunahme der Anzahl der IoT-Geräte wird eine große Menge an Streaming- und historischen Beobachtungsdaten erzeugt. Darüber hinaus wurden mehrere Ontologien, wie die Semantic Sensor Network (SSN) Ontologie, für die semantische Annotation von Beobachtungsdaten vorgeschlagen - entweder Stream oder historisch. Das Resource Description Framework (RDF) ist ein weit verbreitetes Datenmodell zur semantischen Beschreibung der Datensätze. Semantische Annotation bietet ein gemeinsames Verständnis für die Verarbeitung und Analyse von Beobachtungsdaten. Durch das Hinzufügen von Semantik wird die Datengröße jedoch weiter erhöht, insbesondere wenn die Beobachtungswerte von mehreren Geräten redundant erfasst werden. So können beispielsweise mehrere Sensoren Beobachtungen erzeugen, die den gleichen Wert für die relative Luftfeuchtigkeit in einem bestimmten Zeitstempel und einer bestimmten Stadt anzeigen. Diese Situation kann in einem RDF-Diagramm mit vier RDF-Tripel dargestellt werden, wobei Beobachtungen als Tripel dargestellt werden, die das beobachtete Phänomen, die Maßeinheit, den Zeitstempel und die Koordinaten beschreiben. Die RDF-Tripel einer Beobachtung sind mit dem gleichen Thema verbunden. Solche Beobachtungen teilen sich die gleichen Objekte in einer bestimmten Gruppe von Eigenschaften, d.h. sie entsprechen einem Sternmuster, das sich aus diesen Eigenschaften und Objekten zusammensetzt. Wenn die Anzahl dieser Subjektentitäten oder Eigenschaften in diesen Sternmustern groß ist, wird die Größe des RDF-Diagramms und der Abfrageverarbeitung negativ beeinflusst; wir bezeichnen diese Sternmuster als häufige Sternmuster. Diese Arbeit befasst sich mit dem Problem der Identifizierung von häufigen Sternenmustern in RDF-Diagrammen und entwickelt Berechnungsmethoden, um häufige Sternmuster zu identifizieren und ein faktorisiertes RDF-Diagramm zu erzeugen, bei dem die Anzahl der häufigen Sternmuster minimiert wird. Darüber hinaus wenden wir diese faktorisierten RDF-Darstellungen über historische semantische Sensordaten an, die mit der SSN-Ontologie beschrieben werden, und präsentieren tabellarische Darstellungen von faktorisierten semantischen Sensordaten, um Big Data-Frameworks auszunutzen. Darüber hinaus entwickelt diese Arbeit einen wissensbasierten Ansatz namens DESERT, der in der Lage ist, bei Bedarf Streamdaten zu faktorisieren und semantisch anzureichern (on-Demand factorizE and Semantically Enrich stReam daTa). Wir bewerten die Leistung unserer vorgeschlagenen Techniken anhand mehrerer RDF-Diagramm-Benchmarks. Die Ergebnisse zeigen, dass unsere Techniken in der Lage sind, häufige Sternmuster effektiv und effizient zu erkennen, und die Größe der RDF-Diagramme kann um bis zu 66,56% reduziert werden, während die im ursprünglichen RDF-Diagramm dargestellten Daten erhalten bleiben. Darüber hinaus sind die kompakten Darstellungen in der Lage, die Anzahl der RDF-Tripel um mindestens 53,25% in historischen Beobachtungsdaten und bis zu 94,34% in Beobachtungsdatenströmen zu reduzieren. Darüber hinaus reduzieren die Ergebnisse der Anfrageauswertung über historische Daten die Ausführungszeit der Anfrage um bis zu drei Größenordnungen. In Beobachtungsdatenströmen wird die Größe der zur Beantwortung der Anfrage benötigten Daten um 92,53% reduziert, wodurch der Speicherplatzbedarf zur Beantwortung der Anfragen reduziert wird. Diese Ergebnisse belegen, dass IoT-Daten mit den vorgeschlagenen kompakten Darstellungen effizient dargestellt werden können, wodurch die negativen Auswirkungen semantischer Annotationen auf das IoT-Datenmanagement reduziert werden.The Internet of Things (IoT) concept has been widely adopted in several domains to enable devices to interact with each other and perform certain tasks. IoT devices encompass different concepts, e.g., sensors, programs, computers, and actuators. IoT devices observe their surroundings to collect information and communicate with each other in order to perform mutual tasks. These devices continuously generate observational data streams, which become historical data when these observations are stored. Due to an increase in the number of IoT devices, a large amount of streaming and historical observational data is being produced. Moreover, several ontologies, like the Semantic Sensor Network (SSN) Ontology, have been proposed for semantic annotation of observational data-either streams or historical. Resource Description Framework (RDF) is widely adopted data model to semantically describe the datasets. Semantic annotation provides a shared understanding for processing and analysis of observational data. However, adding semantics, further increases the data size especially when the observation values are redundantly sensed by several devices. For example, several sensors can generate observations indicating the same value for relative humidity in a given timestamp and city. This situation can be represented in an RDF graph using four RDF triples where observations are represented as triples that describe the observed phenomenon, the unit of measurement, the timestamp, and the coordinates. The RDF triples of an observation are associated with the same subject. Such observations share the same objects in a certain group of properties, i.e., they match star patterns composed of these properties and objects. In case the number of these subject entities or properties in these star patterns is large, the size of the RDF graph and query processing are negatively impacted; we refer these star patterns as frequent star patterns. This thesis addresses the problem of identifying frequent star patterns in RDF graphs and develop computational methods to identify frequent star patterns and generate a factorized RDF graph where the number of frequent star patterns is minimized. Furthermore, we apply these factorized RDF representations over historical semantic sensor data described using the SSN ontology and present tabular-based representations of factorized semantic sensor data in order to exploit Big Data frameworks. In addition, this thesis devises a knowledge-driven approach named DESERT that is able to on-Demand factorizE and Semantically Enrich stReam daTa. We evaluate the performance of our proposed techniques on several RDF graph benchmarks. The outcomes show that our techniques are able to effectively and efficiently detect frequent star patterns and RDF graph size can be reduced by up to 66.56% while data represented in the original RDF graph is preserved. Moreover, the compact representations are able to reduce the number of RDF triples by at least 53.25% in historical observational data and upto 94.34% in observational data streams. Additionally, query evaluation results over historical data reduce query execution time by up to three orders of magnitude. In observational data streams the size of the data required to answer the query is reduced by 92.53% reducing the memory space requirements to answer the queries. These results provide evidence that IoT data can be efficiently represented using the proposed compact representations, reducing thus, the negative impact that semantic annotations may have on IoT data management

Proposed upgrading to the existing interior design of Muny's Cafe and Bakery at Lot D-22-6A,D-22-6B, D-21-6A,D-21-6B, Jalan Prima Saujana 2/8, Seksyen 2, Taman Prima Saujana, 43000 Kajang for Muny's Sdn Bhd / Farah Adiba Karim

This is a project for upgrading Muny's Cafe & Bakery to make Muny's Cafe & Bakery as a centre for the students in Kajang. This is a good opportunity for Muny's to open a business in Kajang area because it is the students area and to expand its business in Kajang area. Muny’s Cafe & Bakery is located at Lot D-22-6A, D-22-6B, D-21-6A, D-21-6B Jalan Prima Saujana 2/B, Seksyen 2, Taman Prima Saujana, 43000 Kajang Selangor Darul Ehsan. The aim of this project is to create an environment where people can enjoy a joyful delight of freshly-baked bread and buns with affordable price. By using the style of cafe and bakery, upgrading Muny's in becoming a lifestyle cafe. Kajang area is a great site to expand the business. By using the research methods, this project have been identify the need and problems through questionaire, interview, internet and case studies around the proposed site. The issues of this proposed project is the circulation and traffic in existing space which complicates the workers. There is no space provided for the product to display to the customer and the current area also lacks space for a cafe extension and need a proper ventilations and airways. The design objective for this project is to create a space where the workers can work in condusive and comfortable area and proceeding for a good workflow to the working area, to provide a new space fto display their product and also to create a cafe space and proper design ventilation and airways

Mapping Large Scale Research Metadata to Linked Data: A Performance Comparison of HBase, CSV and XML

OpenAIRE, the Open Access Infrastructure for Research in Europe, comprises a database of all EC FP7 and H2020 funded research projects, including metadata of their results (publications and datasets). These data are stored in an HBase NoSQL database, post-processed, and exposed as HTML for human consumption, and as XML through a web service interface. As an intermediate format to facilitate statistical computations, CSV is generated internally. To interlink the OpenAIRE data with related data on the Web, we aim at exporting them as Linked Open Data (LOD). The LOD export is required to integrate into the overall data processing workflow, where derived data are regenerated from the base data every day. We thus faced the challenge of identifying the best-performing conversion approach.We evaluated the performances of creating LOD by a MapReduce job on top of HBase, by mapping the intermediate CSV files, and by mapping the XML output.Comment: Accepted in 0th Metadata and Semantics Research Conferenc

Detection of some virulence genes (esp, agg, gelE, CylA) in Enterococcus faecalis isolated from different clinical cases at Baghdad

The virulent genes are the key players in the ability of the bacterium to cause disease. The products of such genes that facilitate the successful colonization and survival of the bacterium in or cause damage to the host are pathogenicity determinants. This study aimed to investigate the prevalence of virulence factors (esp, agg, gelE, CylA) in E. faecalis isolated from diverse human clinical collected in Iraqi patient , as well as to assess their ability to form biofilm and to determine their haemolytic and gelatinase activities. Thirty-two isolates of bacteria Enterococcus faecalis were obtained, including 15 isolates (46.87%) of the urine, 6 isolates (18.75%) for each of the stool and uterine secretions, and 5 isolates (15.62%) of the wounds from various hospitals in Baghdad, including (Central Children's Hospital, Educational Laboratories, Ibn Al-Baladi Hospital).The isolates were confirmed to belong to the genus E.faecalis after performing morphological and biochemical microscopic examinations and for final diagnosis using the VITEC 2 system. The virulence genes viz. cylA, esp, gelE and agg were recognized in the E. faecalis, and the consequences appeared that the bacteria had eps gene in 32 isolates (100%). As for the agg gene, 32 isolates (100%) were carriers of this gene, which was responsible for these isolates' aptitude to form the biofilm. While for the gelE gene, 27 isolates (84.37%) of the isolates carried this gene, responsible for gelatinase activity whereas, the gene responsible for hemolysis cyl, there were 29 isolates (90.62%) of the total isolates.   The presemce of genes in the isolates would be helpful to determine the colonization and survival of the bacterium in or causing damage to the host

Compacting Frequent Star Patterns in RDF Graphs

Knowledge graphs have become a popular formalism for representing entities and their properties using a graph data model, e.g., the Resource Description Framework (RDF). An RDF graph comprises entities of the same type connected to objects or other entities using labeled edges annotated with properties. RDF graphs usually contain entities that share the same objects in a certain group of properties, i.e., they match star patterns composed of these properties and objects. In case the number of these entities or properties in these star patterns is large, the size of the RDF graph and query processing are negatively impacted; we refer these star patterns as frequent star patterns. We address the problem of identifying frequent star patterns in RDF graphs and devise the concept of factorized RDF graphs, which denote compact representations of RDF graphs where the number of frequent star patterns is minimized. We also develop computational methods to identify frequent star patterns and generate a factorized RDF graph, where compact RDF molecules replace frequent star patterns. A compact RDF molecule of a frequent star pattern denotes an RDF subgraph that instantiates the corresponding star pattern. Instead of having all the entities matching the original frequent star pattern, a surrogate entity is added and related to the properties of the frequent star pattern; it is linked to the entities that originally match the frequent star pattern. We evaluate the performance of our factorization techniques on several RDF graph benchmarks and compare with a baseline built on top of gSpan, a state-of-the-art algorithm to detect frequent patterns. The outcomes evidence the efficiency of proposed approach and show that our techniques are able to reduce execution time of the baseline approach in at least three orders of magnitude reducing the RDF graph size by up to 66.56%

Compacting frequent star patterns in RDF graphs

Knowledge graphs have become a popular formalism for representing entities and their properties using a graph data model, e.g., the Resource Description Framework (RDF). An RDF graph comprises entities of the same type connected to objects or other entities using labeled edges annotated with properties. RDF graphs usually contain entities that share the same objects in a certain group of properties, i.e., they match star patterns composed of these properties and objects. In case the number of these entities or properties in these star patterns is large, the size of the RDF graph and query processing are negatively impacted; we refer these star patterns as frequent star patterns. We address the problem of identifying frequent star patterns in RDF graphs and devise the concept of factorized RDF graphs, which denote compact representations of RDF graphs where the number of frequent star patterns is minimized. We also develop computational methods to identify frequent star patterns and generate a factorized RDF graph, where compact RDF molecules replace frequent star patterns. A compact RDF molecule of a frequent star pattern denotes an RDF subgraph that instantiates the corresponding star pattern. Instead of having all the entities matching the original frequent star pattern, a surrogate entity is added and related to the properties of the frequent star pattern; it is linked to the entities that originally match the frequent star pattern. Since the edges between the entities and the objects in the frequent star pattern are replaced by edges between these entities and the surrogate entity of the compact RDF molecule, the size of the RDF graph is reduced. We evaluate the performance of our factorization techniques on several RDF graph benchmarks and compare with a baseline built on top gSpan, a state-of-the-art algorithm to detect frequent patterns. The outcomes evidence the efficiency of proposed approach and show that our techniques are able to reduce execution time of the baseline approach in at least three orders of magnitude. Additionally, RDF graph size can be reduced by up to 66.56% while data represented in the original RDF graph is preserved

The Impact of Dispositional Optimism and Self-determination on Wellbeing of Job Seeker Young Adults

The present study aimed to find out that 1) dispositional optimism and self-determination are positively related to well-being in job seekers young adults, and 2) to find out the predicting role of dispositional optimism and self-determination in determining the well-being of job seekers. The study was based on a correlational research design. A purposive sample of 192 job seekers young adults aged 19 to 27 years (M =22, SD=1.25) was taken as a sample. The sample consisted of 91 men and 101 women from four different universities in Lahore. Urdu versions of the Life orientation test-revised (Scheier et al., 1985), Self-determination scale (Deci & Ryan, 2000), Mental health continuum short-form (Keyes & Ryff, 1998) and self-constructed demographic information sheet were used for assessment. The results showed that dispositional optimism, self-determination, and well-being are positively related to young job seekers. Further, dispositional optimism and self-determination were found as positive predictors of well-being in job-seeking young adults. Further, the results also indicated that men have higher social well-being as compared to women. The limitations and suggestions are also discussed

Spectacle and Female Power: The Duchess of Malfi in the Sam Wanamaker Playhouse

In 2014, Dominic Dromgoole’s production of The Duchess of Malfi offered the Globe an opportunity to explore the ways in which theatrical intimacy, early modern lighting technology and dramaturgical spectacle could exploit and unearth the performance and meaning-making potential of the Sam Wanamaker Playhouse. Webster’s play raises theatrical challenges that the new indoor playhouse could feasibly and creatively test. This article examines the ways in which the theatrical viability of the Playhouse architecture, its aesthetics and the special effects it enables raised the dramaturgical and thematic stakes of Webster’s most famous tragedy and one of drama’s most famous heroines.En 2014, la mise en scène de La Duchesse d’Amalfi par Dominic Dromgoole fut l’occasion pour le Nouveau Théâtre du Globe d’explorer l’intimité au théâtre, les jeux d’ombres et de lumières et d’exploiter le potentiel dramaturgique et archéologique de sa nouvelle salle : the Sam Wanamaker Playhouse. La pièce de Webster est faite de défis théâtraux que ce théâtre d’intérieur reconstruit à partir des plans des théâtres de la Renaissance anglaise ne pouvait qu’interroger et parfois même élucider. Cet article offre une étude de la viabilité théâtrale de l’architecture de ce théâtre, de son esthétique et des effets spéciaux sur lesquels la pièce de John Webster semblait s’appuyer. Il soulignera aussi l’expérience spectatorielle particulière d’un théâtre d’intérieur ainsi que la relation du public à une héroïne proto-féministe majeure du répertoire de la Renaissance anglaise

A Numerical Solution Algorithm for a Heat and Mass Transfer Model of a Desalination System Based on Packed-Bed Humidification and Bubble Column Dehumidification

The humidification-dehumidification (HDH) desalination system can be advantageous in small-scale, off-grid applications. The main drawback of this technology has been its low energy efficiency, which results in high water production costs. Previous studies have approached this issue through thermodynamic balancing of the system; however, most theoretical work on the balancing of HDH has followed a fixed-effectiveness approach that does not explicitly consider transport processes in the components. Fixing the effectiveness of the heat and mass exchangers allows them to be modeled without explicitly sizing the components and gives insight on how the cycle design can be improved. However, linking the findings of fixed-effectiveness models to actual systems can be challenging, as the performance of the components depends mainly on the available surface areas and the flow rates of the air and water streams. In this study, we present a robust numerical solution algorithm for a heat and mass tranfer model of a complete humidification-dehumidification system consisting of a packed-bed humidifier and a multi-tray bubble column dehumidifier. We look at the effect of varying the water-to-air mass flow rate ratio on the energy efficiency of the system, and we compare the results to those reached following a fixed-effectiveness approach. In addition, we study the effect of the top and bottom temperatures on the performance of the system. We recommended the implementation a control system that varies the mass flow rate ratio in order to keep the system balanced in off-design conditions, especially with varying top temperature.Center for Clean Water and Clean Energy at MIT and KFUPM (Project R4-CW-08

Diversity Of Fusarium Species In Peat Soils

The occurrence and diversity of Fusarium species were determined from 23 peat soil samples collected from peat swamp forest, water-logged peat and peat soils from oil palm plantations. From soil analysis, the peat soils were mostly sandy and loamy sand, acidic (pH 3-4) with low nitrogen and carbon content and low moisture content. Based on the morphological characteristics of macroconidia, microconidia, conidiogenous cells, colony appearance and pigmentation, five Fusarium species were identified namely, F. oxysporum (60%), F. solani (23%), F. proliferatum (14%), F. semitectum (1%) and F. verticillioides (1%). These species are widely distributed worldwide and are common soil inhabitants which act as saprophyte and decomposer. Species identity was confirmed through DNA sequencing of translation elongation factor (TEF-1α). For species from Gibberella fujikuroi species complex, F. verticillioides and F. proliferatum, mating study was conducted. Mating study results showed that nine isolates of F. proliferatum and two isolates of F. verticillioides carried MAT 2 allele. Cross fertility test indicated that nine morphologically identified F. proliferatum were confirmed as F. proliferatum after cross-fertile with mating population D (Gibberella intermedia) and only one isolate was confirmed as F. verticillioides (Gibberella moniliforme) after cross-fertile with mating population A. From phylogenetic analysis using TEF-1α and β-tubulin genes based on individual dataset and combined dataset using neigbour-joining (NJ) and maximum likelihood (ML) methods, showed that the isolates from the same species were clustered in the same clade