Information technology (IT) leaders who do not invest in big data projects may struggle to gain a competitive advantage and business insights to improve performance. Grounded in Kotter’s change and Six Sigma models, the purpose of this qualitative multiple case study was to explore strategies IT leaders use to implement big data analytics successfully. The participants comprised 4 IT leaders from 2 telecommunication organizations in the United States of America, who expertly used big data analytics strategies to promote and maximize competitive advantage. Data were collected from semistructured interviews, company documents, and project-related documents. The collected information was examined by utilizing a thematic analysis approach. Four themes emerged from the data analysis process communication, training, employee involvement in decisions, and teamwork strategy. A key recommendation from these findings is for IT leaders to use successful communication strategies to convey the vision and objectives to all organizational levels. The successful communication-strategy can help evaluate business trends, forecasts, improve overall organizational performance and competitive advantage. The implications for positive social change include the potential for job creation, thus catalysing economic growth within communities.
Spectral clustering has represented a good alternative in digital signal processing and pattern recognition; however a decision concerning the affinity functions among data is still an issue. In this work it is presented an extended version of a traditional multiclass spectral clustering method which employs prior information about the classified data into the affinity matrixes aiming to maintain the background relation that might be lost in the traditional manner, that is using a scaled exponential affinity matrix constrained by weighting the data according to some prior knowledge and via k-way normalized cuts clustering, results in a semi-supervised methodology of traditional spectral clustering. Test was performed over toy data classification and image segmentation and evaluated with and unsupervised performance measures (group coherence, fisher criteria and silhouette).
Deep brain stimulation (DBS) is the most suitable surgical procedure for patients with Parkinson disease whose symptoms are not well controlled by drug treatment, or who cannot tolerate the side-effects of medication. The success of this surgery procedure, depends on the correct location of the neurostimulator device over the basal ganglia area (i.e. thalamus and globus pallidus). This paper presents a software system DEEPBRAINnav as support tool in Parkinson surgery. This software serves as support to the neurological specialist in DBS surgery. DEEPBRAINnav is composed mainly by two applications. First, it allows to the medical specialist, online recognition of brain structures by the analysis of signals from microelectrode recordings (MER), as well as analyses offline databases allowing the inclusion of new trained classifiers for automatic identification of thalamus location. Secondly, the software includes an interactive application to perform the tracking of the microelectrode device, allowing to the neurosurgeon visualize the path and real location of the microelectrode device implant process. DEEPBRAINnav has been tested for Deep Brain Stimulation surgeries performed at the Institute for Epilepsy and Parkinson of the Eje Cafetero (Colombia). The results shows an accurate tracking of the microelectrode implanting process and achieving positive identifications of the Subthalamic Nucleus (STN) over to 85% using a naive Bayes classifier. Furthermore, develop applications for image and signals guide surgery procedures, allows to the neurologist obtain more support information of a surgery procedure given, with the aim to increase the robustness of the DBS surgery.
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