|Type of paper:||Essay|
|Categories:||Data analysis Information technologies Software|
Part A: The KNIME Analytics Platform
The Konstanz Information Miner is an integrated platform that permits simple interactive execution and visual assembly of data pipelines. Over the past years, the demand for a modular analysis of data has increased. Analysis systems need to have some specific features. Firstly, they must be intuitive, easy to use, and enable the user to examine the outcomes visually. Some of the current data pipelining techniques include Insightful Miner, inference, and pipeline pilots. Konstanz Information Miner is designed as a research, collaborative, and teaching podium that allows essay data manipulation, visual methods, and essay integration of new algorithms as new nodes or modules. KNIME enables the users to visually design data flow, selectively implement all or some analysis steps then late on examine the outcomes, interactive views, and models. KNIME is written in Java and depends on Eclipse and utilizes its extension mechanism in adding plugins offering extra functionality.
How this Platform Could be Used to Analyze Health Care Data
KNIME has several features that make it a useful data analysis system. Firstly, this software accepts data from different sources, such as databases, excel workbooks, and other files. This feature means that the system provides its users with many options from which to obtain data. Secondly, after accessing data from different sources, this system allows a user to integrate and use them seamlessly (Dietz & Barthold, 2016). This ability of the system means that a user can analyze data quickly without worrying about its source. Moreover, the software also makes it easy for a user to get the meaning of any data using factors, visual reports that show trends in the data. Finally, the software provides workflows that are easy to follow. Specifically, they include detailed instructions that a person can read and understand easily.
Some of the sources of data that scientists and analysts usually consider are graphical. These people need a system that can handle these images and produce meaningful analysis. The image processing ability of KNIME contains algorithms that can analyze and process images using state-of-the-art libraries (Dietz & Barthold, 2016). The healthcare industry produces a large amount of data, and therefore, an analysis system should be able to handle different types of sources, including images (Raghupathi & Raghypathi, 2014). Another useful factor of this platform that makes it usable for healthcare data analysis is its structure. This platform was created to have an interactive visual frame, modularity, and easy extensibility. These factors improve the functionality of this platform.
Firstly, the Visual interactive frame of the system makes it possible to use drag and drop functionality when combining data from different processing units. It is possible to use individual data pipelines to model customized applications when using this system. Secondly, in the case of modularity, data containers and processing units are independent of each other to allow easy distribution of computation. This independence enables the different algorithm to be developed independently since no type of data is predefined. Therefore, this factor makes it easy to add new types of data to obtain appropriate comparators and renders. Moreover, the new data types are presumably compatible with the old models (Dietz & Barthold, 2016). These factors make KNIME a functional data analysis tool. Finally, this system has easy extensibility, which refers to the ability to add new views or processing nodes and allocating them via a simple plugin technique without having to install it. The data analysis process has pipelines nodes that connect via edges which transport either models or data.
This software is usable in the analysis of the characteristics of a patient, the outcome, and cost the care to select the most cost-effective and clinically treatment methods and provide tools and analysis and hence influence the behavior of the provider. Moreover, it has an intuitive and easy-to-use environment that enables a user to examine the outcomes of the analysis visually. This feature can be used to proactively select individuals that can benefit from lifestyle changes or preventive care. It can also allow the collection and publication of data on medical procedures. This information can enable the patient to select the most effective treatment methods or care regimes that provide the best value
This feature can allow the detection and prevention of fraud by implanting advanced analytics systems to predict and minimize fraud by checking the consistency and accuracy of the claims. Konstanz Information Miner can allow collection and analysis of the clinical record of the patients and claim the sets of data to provide services and information to the third party. For instance, they are licensing of data to help to assist pharmaceutical companies in selecting patients to be included in the clinical trials. Payers develop and deploy mobile apps to assist the patient in managing care, improve care, and locate the providers (Ahmed, 2017). The payers can employ analytics to monitor the patient's adherence to drugs and treatment regimes.
KNIME is also usable in the analysis of big data, which refers to large and complex electronic health data. The size and complexity of this data make it hard to analyze it using old hardware and software. Big data in healthcare is devastating due to its volume and diversity of the types of data and the speed required to manage them (Raghupathi & Raghypathi, 2014). However, KNIME has robust systems that can analyze this big data.
Benefits of this Platform Compared to Similar Commercial Products
KNIME has several features that make it better than other similar commercial systems. Firstly, it enables an analyst to visually gather and acclimatize the flow of analysis from similar building blocks that are then connected through modules or pipes that carry data. Secondly, this system has an intuitive way of reporting its analysis using workflows. The graphical workflow editor of KNIME is implemented in the form of an eclipse nine plugin and it and it provides information about the processes used in the analysis. Extending through data abstraction framework and open API is very easy, and this enables the addition of the new nodes in a transparent manner (Dietz & Barthold, 2016). These features are not available in many other similar commercial systems.
Moreover, KNIME allows an analyst to select data sources, processing steps, build algorithms for modeling, and visualization tools (Triguero et al. 2017). These features make it more potent than other similar applications. KNIME enables the user to model workflows consisting of the nodes, transported through the connection between the nodes. Flow begins with the reading of data from the data source or text files by a node. Special nodes can also query databases. The imported data is kept in internal table-based setups that contain columns with a particular data type (image or integer) and a random number of rows that conform to the specification of the column.
Compared to other pipelining tools, the nodes in KNIME first do the processing of the whole input table then forwards these results to successor nodes. This process has an advantage in that each node can permanently keep its effects. Consequently, it is easy to stop the workflow execution at any node and then resume later. Furthermore, it is possible to inspect the intermediate results any time, and one can insert new nodes that utilize the data that is available without re-executing the preceding nodes. KNIME has a crucial feature called hiliting, which allows an analyst to select rows in a table, after which the table gets highlighted in other views showing displaying similar data. Thus, hiliting is achieved by utilizing a 1:1 correspondence between the unique rows of the tables
The tables of data are sent via connections to other nodes that model, visualize, modify, or transform this data. Modifications may include filtering of rows or columns, controlling the missing values, oversampling, or partitioning of the table into test or training data. After these preparatory steps, it is possible to build a predictive model containing machine learning and data mining algorithms like Naive Bayes classifiers, decision trees, or support vector machines. Several views modes are existing which can be used to inspect the outcomes of workflow analysis. The view modes display data in various ways.
The Konstanz information miller provides a modular framework that offers a graphical worktable for the interactive execution and visual assembly of data pipelines. It has an interactive and powerful user interface that allows easy integration of new nodes or modules. This framework also enables interactive exploration of trained models and analysis of results. It integrates powerful libraries like R statistics software and Weka machine leaning (Triguero et al. 2017). It also has a platform for analyzing various data. This feature can be employed in healthcare to monitor and analyze the results of a given therapy.
Part B: The 4 Vs. of Big Data
The volume of big data refers to a large amount of data related to health care that has been collected over time. This volume comes from the continuous collection of data. There is a previously formidable volume of health care data that comes in the form of radiology images, individual medical records, clinical trial data FDA submissions, data of the genomic sequence of a given population, and human genetics (Raghupathi & Raghypathi, 2014). There are also different systems of big data, for example, genomics, 3D imaging, and biometric sensors.
Velocity refers to the rate with which data is amassed in a real-time manner. The continuous flow of data gathering at exceptional speeds presents a new challenge. Specifically, this data needs to be retrieved, analyzed, compared, and decisions made depending on the output (Raghupathi & Raghypathi, 2014). The velocity of growing data rises with data that represent consistent monitoring, for example, several regular diabetic glucose measurements, more controlled insulin pumps, EKGs, and readings of blood pressure.
The variety of big data refers to the ability to carry out real-time analytics against data of high volume across multiple specialties. The massive variety of data, structured, semi-structured, or unstructured represents an exciting phase in health care (Raghupathi & Raghypathi, 2014). Structured data is data that is simple to store, inquiry, recall, examine, and control by machines. Structure data in EHRS and EMRs include accustomed record fields like the name of the patient, the date of birth, their address, the physician's name, the address, and the hospital name.
Veracity refers to the assurance that data is error-free. This feature implies that the big data, analytics, and results are believable and accurate (Raghupathi & Raghypathi, 2014). Veracity adopts the instantaneous scaling up in the performance and granularity of the platforms and architects, approaches, tools, and algorithms matching the big data requirements. Improving care coordination, reducing costs, and avoiding errors depend on high-quality data. Drug efficacy and safety also rely on the high quality of data.
Dietz, C., & Barthold, M. R. (2016). KNIME for open-source bioimage analysis: A tutorial. In W. De Vos, S. Munch, & J. P. Timmermans (Eds.), Focus on Bio-Image Informatics (Vol. 219, pp. 179-197). Cham: Springer. doi:10.1007/978-3-319-28549-8_7
Ghobani, R., & Ghousi, R. (2019). Predictive data mining approaches in medical diagnosis: A review o...
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