Data Analytic Technology Platforms: Options and TradeoffsJ Singh
This document discusses options for data analytic technology platforms to address big data problems. It begins by distinguishing between problems that truly involve big data versus just large data problems. Examples of big data problems include recommendations, financial analysis, internet security monitoring, social media network analysis, genomics, and sensor data. The key characteristics of big data problems are that the data sets are too large to download, data is generated rapidly requiring near real-time analysis, and the problems involve diverse data types. The document then outlines the governing principle for choosing a platform as processing needing to be close to the data due to data size. Examples of platforms used for different applications are discussed to illustrate this principle. The decision making process for choosing a platform is described as
IABCSeattle Lara Feltin - Biznik - Social MediaIABC Seattle
Lara Eve Feltin presented at IABC/Seattle's Social Media Seminar on March 5, 2009 about the organization of Biznik social networking and community building
Reference Letter - Frank AlvillarJr & Kevin KonstTravis Byakeddy
Travis Byakeddy worked for over 5 years in Residence Life Support Services at Northern Arizona University. His supervisor describes him as responsible, studious, and of the highest character. He was punctual, communicated well with his team, and could handle a heavy workload of school and almost 30 hours of work per week. On many occasions, he worked beyond his scheduled hours to ensure projects were completed properly without overtime pay. Both supervisors fully recommend Byakeddy for any position he applies to based on his strong work ethic and dedication.
The document outlines an investment strategy with short, mid, and long term opportunities in Odesa, Ukraine. The short term strategy involves renovating apartments on Deribasovskaya Street within 2-3 months. The mid term strategy involves larger renovation projects like converting a 1,000 square meter apartment into 7 units over 7-9 months. The long term strategy focuses on hotel acquisitions and extensions, such as expanding an existing hotel over 10 months or converting apartments into a 20 room apartment hotel. Specific projects and financial projections are provided for several opportunity properties.
Data Analytic Technology Platforms: Options and TradeoffsJ Singh
This document discusses options for data analytic technology platforms to address big data problems. It begins by distinguishing between problems that truly involve big data versus just large data problems. Examples of big data problems include recommendations, financial analysis, internet security monitoring, social media network analysis, genomics, and sensor data. The key characteristics of big data problems are that the data sets are too large to download, data is generated rapidly requiring near real-time analysis, and the problems involve diverse data types. The document then outlines the governing principle for choosing a platform as processing needing to be close to the data due to data size. Examples of platforms used for different applications are discussed to illustrate this principle. The decision making process for choosing a platform is described as
IABCSeattle Lara Feltin - Biznik - Social MediaIABC Seattle
Lara Eve Feltin presented at IABC/Seattle's Social Media Seminar on March 5, 2009 about the organization of Biznik social networking and community building
Reference Letter - Frank AlvillarJr & Kevin KonstTravis Byakeddy
Travis Byakeddy worked for over 5 years in Residence Life Support Services at Northern Arizona University. His supervisor describes him as responsible, studious, and of the highest character. He was punctual, communicated well with his team, and could handle a heavy workload of school and almost 30 hours of work per week. On many occasions, he worked beyond his scheduled hours to ensure projects were completed properly without overtime pay. Both supervisors fully recommend Byakeddy for any position he applies to based on his strong work ethic and dedication.
The document outlines an investment strategy with short, mid, and long term opportunities in Odesa, Ukraine. The short term strategy involves renovating apartments on Deribasovskaya Street within 2-3 months. The mid term strategy involves larger renovation projects like converting a 1,000 square meter apartment into 7 units over 7-9 months. The long term strategy focuses on hotel acquisitions and extensions, such as expanding an existing hotel over 10 months or converting apartments into a 20 room apartment hotel. Specific projects and financial projections are provided for several opportunity properties.
This document discusses health issues affecting adolescents and young adults. It notes that good health in adolescence is important for lifelong well-being, but more investment is needed in prevention and early intervention. Some health risks for this age group include mental health problems, substance abuse, nutrition, and social media use. The document also discusses diabetes prevalence worldwide and risk factors for different ethnic groups. It provides information on type 1 and type 2 diabetes. Food and lifestyle choices that impact health are also addressed.
Analytics methods for big data have two requirements above and beyond analytics methods for normal-sized data. First, the analytics can not assume that all the data will fit in memory, or even fit on one server. Second, the choice of analysis methods must avoid high-order algorithms. We illustrate the point with one algorithm: Locality Sensitive Hashing
Vipul Gupta describes his approach to managing subarachnoid hemorrhage (SAH) at his hospital in India. He works closely with neurosurgery as part of an integrated team. For aneurysmal SAH, patients undergo angiography within 24 hours to identify any aneurysms. Aneurysms are often coiled in the same session. Techniques like balloon-assisted coiling are used for complex aneurysms. Flow diverters are used for giant or fusiform aneurysms that cannot be coiled. Aggressive prevention and treatment of vasospasm is emphasized, including continuous intra-arterial nimodipine and milrinone. Overall, this integrated approach aims for good outcomes with over 85
Inventory is an important asset on the balance sheet and impacts the income statement through cost of goods sold. There are two main concerns with inventory valuation - revenue recognition and ending inventory valuation. Companies must count inventory quantities and value them at cost, with adjustments made if the lower of cost or market is below cost. Common inventory costing methods include specific identification, first-in first-out, last-in first-out, and average cost.
Mining of massive datasets using locality sensitive hashing (LSH)J Singh
This document discusses using locality sensitive hashing (LSH) to solve large-scale search problems by clustering similar data points together. It presents an example of using LSH to find Facebook friends with similar interests. The key steps are: (1) representing each user as a vector of interests and computing minhashes, (2) clustering users into buckets based on minhash similarity, and (3) comparing a candidate to others in their bucket to find nearest neighbors. The performance of LSH involves tuning parameters like the number of minhashes and bands to balance false positives and negatives. Implementing LSH on MapReduce can make it scalable to large datasets.
This document discusses health issues affecting adolescents and young adults. It notes that good health in adolescence is important for lifelong well-being, but more investment is needed in prevention and early intervention. Some health risks for this age group include mental health problems, substance abuse, nutrition, and social media use. The document also discusses diabetes prevalence worldwide and risk factors for different ethnic groups. It provides information on type 1 and type 2 diabetes. Food and lifestyle choices that impact health are also addressed.
Analytics methods for big data have two requirements above and beyond analytics methods for normal-sized data. First, the analytics can not assume that all the data will fit in memory, or even fit on one server. Second, the choice of analysis methods must avoid high-order algorithms. We illustrate the point with one algorithm: Locality Sensitive Hashing
Vipul Gupta describes his approach to managing subarachnoid hemorrhage (SAH) at his hospital in India. He works closely with neurosurgery as part of an integrated team. For aneurysmal SAH, patients undergo angiography within 24 hours to identify any aneurysms. Aneurysms are often coiled in the same session. Techniques like balloon-assisted coiling are used for complex aneurysms. Flow diverters are used for giant or fusiform aneurysms that cannot be coiled. Aggressive prevention and treatment of vasospasm is emphasized, including continuous intra-arterial nimodipine and milrinone. Overall, this integrated approach aims for good outcomes with over 85
Inventory is an important asset on the balance sheet and impacts the income statement through cost of goods sold. There are two main concerns with inventory valuation - revenue recognition and ending inventory valuation. Companies must count inventory quantities and value them at cost, with adjustments made if the lower of cost or market is below cost. Common inventory costing methods include specific identification, first-in first-out, last-in first-out, and average cost.
Mining of massive datasets using locality sensitive hashing (LSH)J Singh
This document discusses using locality sensitive hashing (LSH) to solve large-scale search problems by clustering similar data points together. It presents an example of using LSH to find Facebook friends with similar interests. The key steps are: (1) representing each user as a vector of interests and computing minhashes, (2) clustering users into buckets based on minhash similarity, and (3) comparing a candidate to others in their bucket to find nearest neighbors. The performance of LSH involves tuning parameters like the number of minhashes and bands to balance false positives and negatives. Implementing LSH on MapReduce can make it scalable to large datasets.