Horizontal Aggregation Research Articles

Improved Data Mining Analysis by Dataset creation using Horizontal Aggregation and B Tree

Improved Data Mining Analysis by Dataset creation using Horizontal Aggregation and B Tree

Preparation of Datasets for Data Mining Analysis Using Horizontal Aggregation

Preparation of Datasets for Data Mining Analysis Using Horizontal Aggregation

Optimization of Horizontal Aggregation in SQL by using C4. 5 Algorithm

efficient analysis of some data mining system and algorithms, data is required in the horizontal aggregated format. In a relational database, datasets are highly normalized and major efforts are required to compute aggregation when they are expected in horizontal form which is suitable for some data mining, statistical and machine learning algorithm. Query optimization techniques used for vertical (standard) aggregation is not suitable for horizontal aggregation. That's why we propose an optimization technique for horizontal aggregation. To optimize horizontal aggregation we are using C4.5 classification algorithm and query evaluation methods. Horizontal Aggregation represents a template to generate SQL code which automates writing SQL queries, optimizing them, and testing them for correctness. It also reduces manual work in the data preparation phase in a data mining. There are various applications where the horizontal aggregation is used such as electrical billing, banks, hospital management system, pharmacy and online library etc.

A Novel Approach of Horizontal Aggregation in SQL for Data Mining

A Novel Approach of Horizontal Aggregation in SQL for Data Mining

Knowledge Based Secure Data Streaming in Virtual Environment

The process of transferring data within any physical environment is highly vulnerable and liable to face many security issues leading to sniffing of highly confidential data by any of the prevalent cyber attacks. Presently, there are various classic data retrieving methodologies such as CASE, PIVOT and SJP within a physical environment. However, these methods are not completely fail proof against unauthorized data retrieval. This paper aims to implement the concept of Knowledge Cube generation for securely transfer data with the help of Horizontal Aggregation method in virtual environment. The concept of ‘Knowledge Cube’ is referred as a multidimensional model composing of cubes and dimensions, which can be further interpreted so as to execute complex queries during any data retrieval process in both physical and virtual environment.

Preparing Data Sets for the Data Mining Analysis using the Most Efficient Horizontal Aggregation Method in SQL

A huge amount of time is needed for making the dataset for the data mining analysis because data mining practitioners required to write complex SQL queries and many tables are to be joined to get the aggregated result. The traditional SQL aggregations prepare the data sets in vertical layout that is; they return result on one column per aggregated group. But for the data mining project, the data set to be required in horizontal layout. In order to transform the data into suitable form the existing three horizontal aggregation methods are used. The existing method for evaluating horizontal aggregation are SPJ (select, project, join) method, CASE method and PIVOT method. The analysis become more efficient if the dataset obtained is in the horizontal form. The main aim is to identify the most efficient method from these three methods in terms of time and space complexity. So these methods are compared using large tables and identified that the CASE method is more efficient than SPJ and PIVOT method.

Optimization of Horizontal Aggregation in SQL by using C4.5 Algorithm and K-Means Clustering

Datasets in the horizontal aggregated layout are preferred by most of data mining algorithms, machine learning algorithm. Major efforts are required to compute data in the horizontal aggregated format. There are many inbuilt aggregation functions in SQL, namely, minimum, maximum, average, sum and count. These aggregation functions are used with a query evaluation method to retrieve data in the horizontal aggregation format. Optimization techniques used for vertical aggregation is not appropriate for horizontal aggregation. Standard aggregations are hard to interpret when there are many result rows, especially when grouping attributes having high cardinalities. That is why we proposed C4.5 classification algorithm and K- means clustering algorithm with query evaluation method and aggregation function for optimizing horizontal aggregation. Horizontal aggregation is a method which generates SQL code to return aggregated columns in the horizontal tabular layout. It returns a set of numbers instead of one number per row. There are various applications where the horizontal aggregation is used such as electrical billing, banks, hospital management system, pharmacy, and online library etc. (6).

The Development and Evolution of Two Atmospheric Rivers in Proximity to Western North Pacific Tropical Cyclones in October 2010

AbstractThis study investigates the evolution of two zonally elongated atmospheric rivers (ARs) that produced >200 mm of rainfall over mountainous regions of Northern California in late October 2010. Synoptic-scale analysis and air parcel trajectory analysis indicate that the ARs developed within high-CAPE environments characterized by troposphere-deep ascent as water vapor was transported directly from western North Pacific tropical cyclones (TCs) toward the equatorward entrance region of an intensifying North Pacific jet stream (NPJ). The same ARs were subsequently maintained as water vapor was transported from extratropical and subtropical regions over the central and eastern North Pacific in an environment characterized by quasigeostrophic forcing for ascent and strong frontogenesis along the anticyclonic shear side of an intense and zonally extended NPJ. Although the ARs developed in conjunction with water vapor transported from regions near TCs and in the presence of troposphere-deep ascent, an atmospheric water vapor budget illustrates that decreases in integrated water vapor (IWV) via precipitation are largely offset by the horizontal aggregation of water vapor along the AR corridors via IWV flux convergence in the presence of frontogenesis. The frameworks used for investigations of predecessor rain events ahead of TCs and of interactions between recurving TCs and the NPJ are also utilized to illustrate many dynamically similar processes related to AR development and evolution. Similarities include the following: water vapor transport directly from a TC, troposphere-deep ascent in a high-CAPE environment beneath the equatorward entrance region of an intensifying upper-tropospheric jet streak, interactions between diabatic outflow and an upper-tropospheric jet streak, and strong frontogenesis.

K Means Clustering Algorithm for Partitioning Data Sets Evaluated From Horizontal Aggregations

Data mining refers to the process of analyzing the data from different perspectives and summarizing it into useful information that is mostly used by the different users for analyzing the data as well as for preparing data sets. A data set is collection of data that is present in the tabular form. Preparing data set involves complex SQL queries, joining tables and aggregate functions. Traditional RDBMS manages the tables with vertical format and returns one number per row. It means that it returns a single value output which is not suitable for preparing a data set. This paper mainly focused on k means clustering algorithm which is used to partition data sets after horizontal aggregations and a small description about the horizontal aggregation methods which returns set of numbers instead of one number per row. This paper consists of three methods that is SPJ, CASE and PIVOT methods in order to evaluate horizontal aggregations. Horizontal aggregations results in large volumes of data sets which are then partitioned into homogeneous clusters is important in the system. This can be performed by k means clustering algorithm.

Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables, and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g., point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, whereas the SPJ method does not.

Value Relevance of Proportionate Consolidation Versus the Equity Method: Evidence from Hong Kong

Whether proportionate consolidation (PC) or the equity method (EM) provides more informative financial statements has been controversial. This study uses data from listed companies in Hong Kong to investigate the value relevance of EM compared to PC during 2005-2008 when the local word-for-word equivalent HKAS 31 offered the same options. The results of this study provide evidence that PC does not offer higher value relevance than EM. PC’s horizontal aggregation of a portion of the operations, assets and liabilities of the jointly controlled entities (JCEs) with those of the venturer is less informative to investors than EM’s vertical aggregation.

Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis

Data mining is widely used domain for extracting trends or patterns from historical data. However, the databases used by enterprises can't be directly used for data mining. It does mean that Data sets are to be prepared from real world database to make them suitable for particular data mining operations. However, preparing datasets for analyzing data is tedious task as it involves many aggregating columns, complex joins, and SQL queries with sub queries. More over the existing aggregations performed through SQL functions such as MIN, MAX, COUNT, SUM, AVG return a single value output which is not suitable for making datasets meant for data mining. In fact these aggregate functions are generating vertical aggregations. This paper presents techniques to support horizontal aggregations through SQL queries. The result of the queries is the data which is suitable for data mining operations. It does mean that this paper achieves horizontal aggregations through some constructs built that includes SQL queries as well. The methods prepared by this paper include CASE, SPJ and PIVOT. We have developed a prototype application and the empirical results reveal that these constructs are capable of generating data sets that can be used for further data mining operations. Index Terms - Aggregations, SQL, data mining, OLAP, and data set generation.s

Two-dimensional trust rating aggregations in service-oriented applications

In most existing trust evaluation studies, a single value is computed based on the ratings given to a service provider to indicate the current trust level. This is useful but may not represent the trust features well under certain circumstances. Alternatively, a complete set of trust ratings can be transferred to a service client for local trust calculation. But this obviously creates a big overhead in communication as the data set usually has a large size covering a long service history. In this paper, we present a novel two-dimensional aggregation approach consisting of both vertical and horizontal aggregations of trust ratings. The vertical aggregation calculates the aggregated rating representing the trust level for the services delivered in a small time period. The horizontal aggregation applies our proposed optimal algorithm to determine the minimal number of time intervals, within each of which a trust vector with three values can be calculated to represent all the ratings in that time interval and retain the trust features well. Hence, a small set of trust vectors can represent a large set of trust ratings. This is significant for large-scale trust rating transmission and trust evaluation. Experiments have been conducted to illustrate the properties of our proposed approach.

Node Aggregation Degree-Aware Random Routing for Non-uniform Wireless Sensor Networks

Routing is still a challenging issue for wireless sensor networks (WSNs), in particular for WSNs with a non-uniform deployment of nodes. This paper introduces a Node Aggregation Degree-aware Random Routing (NADRR) algorithm for non-uniform WSNs with the help of two new concepts, namely the Local Vertical Aggregation Degree (LVAD) and Local Horizontal Aggregation Degree (LHAD). Our basic idea is to first apply the LVAD and LHAD to determine one size-proper forwarding region (rather than a fixed-size one as in uniform node deployment case) for each node participating in routing, then select the next hop node from the size-proper forwarding region in a probabilistic way, considering both the residual energy and distribution of nodes. In this way, a good adaptability to the non-uniform deployment of nodes can be guaranteed by the new routing algorithm. Extensive simulation results show that in comparison with other classical geographic position based routing algorithms, such as GPSR, TPGF and CR, the proposed NADRR algorithm can result in lower node energy consumption, better balance of node energy consumption, higher routing success rate and longer network lifetime.

Cell growth and Trametes versicolor laccase production in transformed Pichia pastoris cultured by solid‐state or submerged fermentations

AbstractBACKGROUND: Growth kinetics of Pichia pastoris and heterologous expression of Trametes versicolor laccase were compared. This is the first study of its kind between solid‐state yeast cultures done on polyurethane foam (PUF) and submerged liquid fermentations (SmF).RESULTS: The maximum values of biomass were similar for SSF (solid‐state fermentation) and SmF experiments when the BOD (biochemical oxygen demand) was lower than 100 g L−1. For higher BOD levels, the maximum values of biomass were 55 g L−1 (SSF) and 35 g L−1 (SmF). Micrographs of PUF preparations showed yeast growing within liquid lamellae, thinner than 100 µm, forming large horizontal aggregates. Yeast aggregates were much smaller in SmF than in SSF experiments; however, laccase expression was lower in PUF than in SmF, unless the methanol concentration was increased to 63 g L−1, which was inhibitory only to the SmF system.CONCLUSION: The results show that oxygen mass transfer is more efficient in SSF, which is related to the higher area/volume ratio compared with SmF. Induction differences may also be due to hindered diffusion of methanol within large yeast aggregates. Copyright © 2009 Society of Chemical Industry

Caracterización de agregaciones de merluza de cola (Macruronus magellanicus) en la zona centro-sur de Chile

A monthly characterization of Chilean hoki aggregations was done using acoustic records ob- tained with SIMRAD EK60 and ES60 devices aboard three multipurpose vessels that operated in the central- southern fishery unit during 2003; this considered morphological and bathymetric descriptors. The wide mor- phological variation of the aggregations was explained by their cohesion level and tendency to form large horizontal aggregations whereas, bathymetrically, 90% of the aggregations were located between 300 and 450 m. Evidence of associations between various descriptors was found, and these were, in turn, associated with other factors such as mean size, water temperature, latitude, and month, allowing an adequate description of the shape of the aggregations and their location in the water column. The relevant relationships were: aggrega- tion depth with regard to bottom depth (r = 0.55), aggregation altitude with regard to bottom depth (r = 0.72), aggregation altitude with respect to the relative depth index (r = -0.98), and aggregation depth with respect to mean size (r = 0.66).

Impact of Scale and Aggregation on the Terrestrial Water Exchange: Integrating Land Surface Models and Rhône Catchment Observations

Abstract Land surface models (LSMs) used in climate modeling include detailed above-ground biophysics but usually lack a good representation of runoff. Both processes are closely linked through soil moisture. Soil moisture however has a high spatial variability that is unresolved at climate model grid scales. Physically based vertical and horizontal aggregation methods exist to account for this scaling problem. Effects of scaling and aggregation have been evaluated in this study by performing catchment-scale LSM simulations for the Rhône catchment. It is found that evapotranspiration is not sensitive to soil moisture over the Rhône but it largely controls total runoff as a residual of the terrestrial water balance. Runoff magnitude is better simulated when the vertical soil moisture fluxes are resolved at a finer vertical resolution. The use of subgrid-scale topography significantly improves both the timing of runoff on the daily time scale (response to rainfall events) and the magnitude of summer baseflow (from seasonal groundwater recharge). Explicitly accounting for soil moisture as a subgrid-scale process in LSMs allows one to better resolve the seasonal course of the terrestrial water storage and makes runoff insensitive to the used grid scale. However, scale dependency of runoff to above-ground hydrology cannot be ignored: snowmelt runoff from the Alpine part of the Rhône is sensitive to the spatial resolution of the snow scheme, and autumnal runoff from the Mediterranean part of the Rhône is sensitive to the spatial resolution of precipitation.

Seed banks: ecological definitions and sampling considerations

Definitions of seed banks are discussed in the introductory part of the paper. In the second part, a literature review regarding sampling problems in soil seed bank ecology is presented. Regarding sampling depth, a rapid decline in soil seed content is demonstrated from example studies. The use of soil cores with 5 cm or 10 cm depth is suggested to ensure comparability of results. For determination of optimal sample volumes for various communities, the species saturation model is suggested such that “minimal volume” can be defined for soil seed banks in the same way that “minimal area” can be defined in phytosociological studies. Although sampling time may depend on research goals for vegetation types with a winter standstill period, late autumn sampling is suggested for detecting the entire soil seed bank, whilst late spring sampling is recommended for the examination of its persistent part. Studies looking at medium (plant community level) and fine scales (patch level) have demonstrated that soil seed bank distributions show horizontal aggregation for most of the cases and for most of the species. Seed dispersal processes which are among major factors responsible for such aggregated patterns are also discussed.

Stability of 2-D Colloidal Particle Aggregates Held against Flow Stress in an Ultrasound Trap

The formation of a two-dimensional aggregate of 25 microm latex particles in a 1.5 MHz ultrasound standing wave (USW) field and its disintegration in a flow were studied. The aggregate was held in the pressure node plane, which allowed continuous microscope observation and video recording of the processes. The trajectories and velocities of the particles approaching the formation site were analyzed by particle image velocimetry (PIV). Since the direct radiation force on the particles dominated the drag due to acoustic streaming, the acoustic pressure profile in the vicinity of the aggregate was quantifiable. The drag coefficients D(coef) for 2- to 485-particle aggregates were estimated from the balance of the drag force FD and the buoyancy-corrected gravitational force during sedimentation on termination of the ultrasound when the long axis of the aggregate was in the vertical plane. D(coef) were calculated from FD as proportional to the aggregate velocity. Experiments on particle detachment by flow (in-plane velocity measured by PIV) from horizontal aggregates suspended in deionized water and CaCl2 solution of different concentrations showed that the mechanical strength of the aggregates depended on the acoustic pressure amplitude P0 and ionic strength of the solution. In deionized water the flow velocity required to detach the first single particle from an aggregate increased from 1 mm s-1 at P0 = 0.6 MPa to 4.2 mm s-1 at P0 = 1.4 MPa. The balance of forces acting on particles in a USW trap is discussed. The magnitude of the shear stress employed ( approximately 0.05 Pa) and separation forces suggests that this technique can be applied to studying the mechanical responses of cell aggregates to hydrodynamic flow, where cell-cell interaction can be separated from the effects of solid substrata.

Influence of monsoons and oceanographic processes on red tides in Hong Kong waters

Hong Kong waters in the northern part of the South China Sea are subjected to seasonal oceanographic processes due to monsoonal winds and Pearl River outflow. Several 100 red tides have occurred in Hong Kong waters during 1983 to 1998, and show a clear spatial and temporal distribution. Most (74%) occurred in semi-enclosed NE bays away from the Pearl River estuary, and fewer occurred in western estuarine waters. Most red tides (70%) occurred between December and May, and fewer in summer. However, nutrients are high in the Pearl River estuary, whereas nutrients are generally low in the NE bays (Mirs Bay and Port Shelter) and cannot support the high biomass of red tides. This suggests that there are concentrating mechanisms promoting the formation of red tides in NE waters, either by vertical migration or horizontal aggregation under suitable wind conditions (NE winds and moderate speeds around 6 m s(-1)). The prevailing NE monsoon winds in winter and spring result in downwelling and a longer residence time of waters in the NE bays, creating conditions similar to those of a batch culture, and allowing local inputs of nutrients and vertical migration of phytoplankton in shallow waters to play a dominant role in favor of local red tides. This may be one of the reasons why more red tides occur in winter and spring in semi-enclosed waters. In summer, the SW monsoon winds result in upwelling along the coast, and high river discharge and rainfall cause an increased estuarine circulation in the Pearl River estuary and rapid outflow of the surface water from these semi-enclosed waters. As a result, residence time of these waters decreases, and they may be analogous to semi-continuous or continuous cultures. This may explain why there are fewer red tides in summer. The species occurring most frequently are (in descending order) Noctiluca scintillans, Gonyaulax polygramma, Skeletonema costatum, Mesodinium rubrum, Prorocentrum minimum and Ceratium furca. In general, dinoflagellate red tides occur mostly in April when Si is low, whereas diatom red tides occur in June when Si increases due to the freshwater discharge. Deep oceanic water on the continental shelf is drawn into the NE bays during the upwelling; it is poor in nutrients, and does not favor nutrient acquisition in deep water by vertical migrating dinoflagellates.