Modeling Large Time Series for Efficient Approximate Query Processing

Evolving customer requirements and increasing competition force business organizations to store increasing amounts of data and query them for information at any given time. Due to the current growth of data volumes, timely extraction of relevant information becomes more and more difficult with traditional methods. In addition, contemporary Decision Support Systems (DSS) favor faster approximations over slower exact results. Generally speaking, processes that require exchange of data become inefficient when connection bandwidth does not increase as fast as the volume of data. In order to tackle these issues, compression techniques have been introduced in many areas of data processing. In this paper, we outline a new system that does not query complete datasets but instead utilizes models to extract the requested information. For time series data we use Fourier and Cosine transformations and piece-wise aggregation to derive the models. These models are initially created from the original data and are kept in the database along with it. Subsequent queries are answered using the stored models rather than scanning and processing the original datasets. In order to support model query processing, we maintain query statistics derived from experiments and when running the system. Our approach can also reduce communication load by exchanging models instead of data. To allow seamless integration of model-based querying into traditional data warehouses, we introduce a SQL compatible query terminology. Our experiments show that querying models is up to 80 % faster than querying over the raw data while retaining a high accuracy

Fungal diversity notes 929–1035: taxonomic and phylogenetic contributions on genera and species of fungi

This article is the ninth in the series of Fungal Diversity Notes, where 107 taxa distributed in three phyla, nine classes, 31 orders and 57 families are described and illustrated. Taxa described in the present study include 12 new genera, 74 new species, three new combinations, two reference specimens, a re-circumscription of the epitype, and 15 records of sexualasexual morph connections, new hosts and new geographical distributions. Twelve new genera comprise Brunneofusispora, Brunneomurispora, Liua, Lonicericola, Neoeutypella, Paratrimmatostroma, Parazalerion, Proliferophorum, Pseudoastrosphaeriellopsis, Septomelanconiella, Velebitea and Vicosamyces. Seventy-four new species are Agaricus memnonius, A. langensis, Aleurodiscus patagonicus, Amanita flavoalba, A. subtropicana, Amphisphaeria mangrovei, Baorangia major, Bartalinia kunmingensis, Brunneofusispora sinensis, Brunneomurispora lonicerae, Capronia camelliaeyunnanensis, Clavulina thindii, Coniochaeta simbalensis, Conlarium thailandense, Coprinus trigonosporus, Liua muriformis, Cyphellophora filicis, Cytospora ulmicola, Dacrymyces invisibilis, Dictyocheirospora metroxylonis, Distoseptispora thysanolaenae, Emericellopsis koreana, Galiicola baoshanensis, Hygrocybe lucida, Hypoxylon teeravasati, Hyweljonesia indica, Keissleriella caraganae, Lactarius olivaceopallidus, Lactifluus midnapurensis, Lembosia brigadeirensis, Leptosphaeria urticae, Lonicericola hyaloseptispora, Lophiotrema mucilaginosis, Marasmiellus bicoloripes, Marasmius indojasminodorus, Micropeltis phetchaburiensis, Mucor orantomantidis, Murilentithecium lonicerae, Neobambusicola brunnea, Neoeutypella baoshanensis, Neoroussoella heveae, Neosetophoma lonicerae, Ophiobolus malleolus, Parabambusicola thysanolaenae, Paratrimmatostroma kunmingensis, Parazalerion indica, Penicillium dokdoense, Peroneutypa mangrovei, Phaeosphaeria cycadis, Phanerochaete australosanguinea, Plectosphaerella kunmingensis, Plenodomus artemisiae, P. lijiangensis, Proliferophorum thailandicum, Pseudoastrosphaeriellopsis kaveriana, Pseudohelicomyces menglunicus, Pseudoplagiostoma mangiferae, Robillarda mangiferae, Roussoella elaeicola, Russula choptae, R. uttarakhandia, Septomelanconiella thailandica, Spencermartinsia acericola, Sphaerellopsis isthmospora, Thozetella lithocarpi, Trechispora echinospora, Tremellochaete atlantica, Trichoderma koreanum, T. pinicola, T. rugulosum, Velebitea chrysotexta, Vicosamyces venturisporus, Wojnowiciella kunmingensis and Zopfiella indica. Three new combinations are Baorangia rufomaculata, Lanmaoa pallidorosea and Wojnowiciella rosicola. The reference specimens of Canalisporium kenyense and Tamsiniella labiosa are designated. The epitype of Sarcopeziza sicula is re-circumscribed based on cyto- and histochemical analyses. The sexual-asexual morph connection of Plenodomus sinensis is reported from ferns and Cirsium for the first time. In addition, the new host records and country records are Amanita altipes, A. melleialba, Amarenomyces dactylidis, Chaetosphaeria panamensis, Coniella vitis, Coprinopsis kubickae, Dothiorella sarmentorum, Leptobacillium leptobactrum var. calidus, Muyocopron lithocarpi, Neoroussoella solani, Periconia cortaderiae, Phragmocamarosporium hederae, Sphaerellopsis paraphysata and Sphaeropsis eucalypticola

Knowledge on heart patients through stethoscopic cardiac murmur identification for E-Healthcare

Cardiac murmurs are the sounds produced by the abnormal passage of the blood flow inside the heart due to the diseased valves or tissues. Identifying the type of the murmur can diagnose the cardiac pathology related to that particular murmur. This paper describes a researched digital stethoscope that can be used as a stethoscope as well as a murmur identification device. To capture the heart beat, a chest piece of a traditional stethoscope is used with a condenser microphone attachment. Then the signal is amplified and Alters out the noise and sent to the ARM processor. At the processing module, sampling is carried out and stores the sampled data if the data transmission is not being done. Then the sampled data is transmitted to a central location using radio frequency transmitter module to carry out the signal processing. If the user wishes, the signal processing can be done in the stethoscope itself since the ARM processor is in the stethoscope unit

Efficient Approximate OLAP Querying Over Time Series

The ongoing trend for data gathering not only produces larger volumes of data, but also increases the variety of recorded data types. Out of these, especially time series, e.g. various sensor readings, have attracted attention in the domains of business intelligence and decision making. As OLAP queries play a major role in these domains, it is desirable to also execute them on time series data. While this is not a problem on the conceptual level, it can become a bottleneck with regards to query run-time. In general, processing OLAP queries gets more computationally intensive as the volume of data grows. This is a particular problem when querying time series data, which generally contains multiple measures recorded at fine time granularities. Usually, this issue is addressed either by scaling up hardware or by employing workload based query optimization techniques. However, these solutions are either costly or require continuous maintenance. In this paper we propose an approach for approximate OLAP querying of time series that offers constant latency and is maintenance-free. To achieve this, we identify similarities between aggregation cuboids and propose algorithms that eliminate the redundancy these similarities present. In doing so, we can achieve compression rates of up to 80% while maintaining low average errors in the query results

The Faces of Fungi database: fungal names linked with morphology, phylogeny and human impacts

Taxonomic names are key links between various databases that store information on different organisms. Several global fungal nomenclural and taxonomic databases (notably Index Fungorum, Species Fungorum and MycoBank) can be sourced to find taxonomic details about fungi, while DNA sequence data can be sourced from NCBI, EBI and UNITE databases. Although the sequence data may be linked to a name, the quality of the metadata is variable and generally there is no corresponding link to images, descriptions or herbarium material. There is generally no way to establish the accuracy of the names in these genomic databases, other than whether the submission is from a reputable source. To tackle this problem, a new database (FacesofFungi), accessible at www.​facesoffungi.​org (FoF) has been established. This fungal database allows deposition of taxonomic data, phenotypic details and other useful data, which will enhance our current taxonomic understanding and ultimately enable mycologists to gain better and updated insights into the current fungal classification system. In addition, the database will also allow access to comprehensive metadata including descriptions of voucher and type specimens. This database is user-friendly, providing links and easy access between taxonomic ranks, with the classification system based primarily on molecular data (from the literature and via updated web-based phylogenetic trees), and to a lesser extent on morphological data when molecular data are unavailable. In FoF species are not only linked to the closest phylogenetic representatives, but also relevant data is provided, wherever available, on various applied aspects, such as ecological, industrial, quarantine and chemical uses. The data include the three main fungal groups (Ascomycota, Basidiomycota, Basal fungi) and fungus-like organisms. The FoF webpage is an output funded by the Mushroom Research Foundation which is an NGO with seven directors with mycological expertise. The webpage has 76 curators, and with the help of these specialists, FoF will provide an updated natural classification of the fungi, with illustrated accounts of species linked to molecular data. The present paper introduces the FoF database to the scientific community and briefly reviews some of the problems associated with classification and identification of the main fungal groups. The structure and use of the database is then explained. We would like to invite all mycologists to contribute to these web pages.Fil: Jayasiri, Subashini C.. Mae Fah Luang University. Center of Excellence in Fungal Research; TailandiaFil: Hyde, Kevin D.. Mae Fah Luang University. Center of Excellence in Fungal Research; Tailandia. World Agro forestry Centre East and Central Asia Office; China. King Saud University. College of Science. Botany and Microbiology Department; Arabia SauditaFil: Ariyawansa, Hiran A.. Mae Fah Luang University. Center of Excellence in Fungal Research; Tailandia. Guizhou Academy of Agricultural Sciences. Guizhou Key Laboratory of Agricultural Biotechnology; ChinaFil: Bhat, Jayarama. Goa University. Department of Botany; IndiaFil: Buyck, Bart. Museum National D; FranciaFil: Romero, Andrea Irene. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Micología y Botánica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Micología y Botánica; ArgentinaFil: Taylor, Joanne E.. Royal Botanic Gardens; Reino UnidoFil: Tsui, Clement K. M.. University Of British Columbia; CanadáFil: Vizzini, Alfredo. University of Turin. Department of Life Sciences and Systems Biology; ItaliaFil: Abdel wahab, Mohamed A.. Sohag University. Faculty of Science. Department of Botany and Microbiology; EgiptoFil: Wen, Tingchi. Guizhou University. Ministry of Education. Engineering Research Center of Southwest Bio-Pharmaceutical Resources; ChinaFil: Boonmee, Saranyaphat. Mae Fah Luang University. Center of Excellence in Fungal Research; TailandiaFil: Dai, Dong Qin. Mae Fah Luang University. Center of Excellence in Fungal Research; Tailandia. World Agro forestry Centre East and Central Asia Office; ChinaFil: Daranagama, Dinushani A.. Mae Fah Luang University. Center of Excellence in Fungal Research; Tailandia. Chinese Academy of Sciences. Institute of Microbiology. State Key Laboratory of Mycology; ChinaFil: Dissanayake, Asha J.. Mae Fah Luang University. Center of Excellence in Fungal Research; Tailandia. Beijing Academy of Agriculture and Forestry Sciences. Institute of Plant and Environment Protection; ChinaFil: Ekanayaka, Anusha H.. Mae Fah Luang University. Center of Excellence in Fungal Research; TailandiaFil: Fryar, S. C.. Flinders University. School of Biology; AustraliaFil: Hongsanan, Sinang. Mae Fah Luang University. Center of Excellence in Fungal Research; TailandiaFil: Jayawardena, Ruvishika S.. Mae Fah Luang University. Center of Excellence in Fungal Research; Tailandia. Beijing Academy of Agriculture and Forestry Sciences. Institute of Plant and Environment Protection; ChinaFil: Li, Wenjing. Mae Fah Luang University. Center of Excellence in Fungal Research; Tailandia. World Agro forestry Centre East and Central Asia Office; ChinaFil: Perera, Rekhani H.. Mae Fah Luang University. Center of Excellence in Fungal Research; TailandiaFil: Phookamsak, R.. Mae Fah Luang University. Center of Excellence in Fungal Research; TailandiaFil: Silva, Nimali I. de. Chiang Mai University. Faculty of Science. Department of Biology; TailandiaFil: Thambugala, Kasun M.. Mae Fah Luang University. Center of Excellence in Fungal Research; Tailandia. Guizhou Academy of Agricultural Sciences. Guizhou Key Laboratory of Agricultural Biotechnology; ChinaFil: Tian, Qing. Mae Fah Luang University. Center of Excellence in Fungal Research; Tailandia. World Agro forestry Centre East and Central Asia Office; ChinaFil: Wijayawardene, Nalin N.. Mae Fah Luang University. Center of Excellence in Fungal Research; Tailandia. Guizhou University. Ministry of Education. Engineering Research Center of Southwest Bio-Pharmaceutical Resources; ChinaFil: Zhao, Ruilin. Chinese Academy of Sciences. Institute of Microbiology. State Key Laboratory of Mycology; ChinaFil: Zhao, Qi. World Agro forestry Centre East and Central Asia Office; China. Yunnan Academy of Agricultural Science. Biotechnology and Germplasm Resources Institute; ChinaFil: Kang, Jichuan. Guizhou University. Ministry of Education. Engineering Research Center of Southwest Bio-Pharmaceutical Resources; ChinaFil: Promputtha, Itthayakorn. Chiang Mai University. Faculty of Science. Department of Biology; Tailandi

Fungal diversity notes 1151-1276: taxonomic and phylogenetic contributions on genera and species of fungal taxa

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