Big data can be useful in stopping crimes as it can be used to predict who is more likely to commit crime and in which situation and circumstances a crime is likely to occur. This can therefore prevent crime as police could be more informed about if a crime is likely to occur and they can take precautions to prevent it from happening.
It is not completely reliable to use big data to stop crime as there are disadvantages - we can't solely use big data because we would just be making guesses as to which crimes are going to happen and we cannot be sure. Big data is also unlikely to remove unpredictability of crimes as many criminals come as a surprise. It may also put innocent people under suspicion wrongly if they appear to meet the criteria of an average criminal even though they are law abiding.
Wednesday, June 5, 2019
Applications of Big Data Use for Business
People opinions (on current/upcoming product)-
We can quickly gather opinions of products from a large group of people. This aids businesses in decision making for future as it can tweak the product to suit the opinions gathered from the public. This can lead to more sales as the product can be suited to the opinions of people, making them more likely to buy the product. Finding out people's opinions can help businesses understand what is popular in general.
Targeted Advertising - targeted advertisements are shown to people which are more likely to be relevant to the person. Allows person to slightly change what the data says so the person seeing it will like it. It is designed to try to make sure money for ads are being spent wisely. Shows you the products your most likely to be interested in so there's more chance you will engage with the ad and ultimately spend money. Business can purchase data so they know what you are more interested in so they can show specialised ads for you
We can quickly gather opinions of products from a large group of people. This aids businesses in decision making for future as it can tweak the product to suit the opinions gathered from the public. This can lead to more sales as the product can be suited to the opinions of people, making them more likely to buy the product. Finding out people's opinions can help businesses understand what is popular in general.
Targeted Advertising - targeted advertisements are shown to people which are more likely to be relevant to the person. Allows person to slightly change what the data says so the person seeing it will like it. It is designed to try to make sure money for ads are being spent wisely. Shows you the products your most likely to be interested in so there's more chance you will engage with the ad and ultimately spend money. Business can purchase data so they know what you are more interested in so they can show specialised ads for you
Applications of Big Data Use for Science
Live/Historic Data For Research - live data would be data produced within the past week and historic data can go way back hundreds of years. A lot of data is available for scientists to analyse. This can help science move forward as we find out the most efficient way to do things and we can make discoveries. An example would be pharmaceutical companies can use big data to alter their medicine. They could find out if a medicine worked from looking at facebook posts from users who talk about it.
Finding and collating research papers (and their findings) - some studies are quite small and some are not public. If you want to find out as much information as you can from studies, the best way to find out is word of mouth. Looking at all the small studies spoken about through social media can give you a bigger idea of the wider population.
Finding and collating research papers (and their findings) - some studies are quite small and some are not public. If you want to find out as much information as you can from studies, the best way to find out is word of mouth. Looking at all the small studies spoken about through social media can give you a bigger idea of the wider population.
Data Mining Methods
Data is extracted in order to find some sort of pattern in the data set. There are different levels of analysis we ca
Association rule mining: this is when the program tries to see if there is a relation between two or more variables from data. It helps find the probability of relations between variables in large sets of data by making associations from the relations found.
Correlation analysis: this is used to see how strong the relationship between two variables is. It can either be a positive or negative correlation. Positive correlation is when x goes up y goes up but negative correlation is when x goes up and y goes down (on a graph).
regression analysis: this allows you to study the relationship between two or more variables. In this type of analysis one or more variables is dependent on one variable. Points are plotted on a graph and a regressive line is drawn (or line of best fit) and this can help predict the future as we see which direction the line is heading.
Association rule mining: this is when the program tries to see if there is a relation between two or more variables from data. It helps find the probability of relations between variables in large sets of data by making associations from the relations found.
Correlation analysis: this is used to see how strong the relationship between two variables is. It can either be a positive or negative correlation. Positive correlation is when x goes up y goes up but negative correlation is when x goes up and y goes down (on a graph).
regression analysis: this allows you to study the relationship between two or more variables. In this type of analysis one or more variables is dependent on one variable. Points are plotted on a graph and a regressive line is drawn (or line of best fit) and this can help predict the future as we see which direction the line is heading.
Monday, June 3, 2019
Characteristics of Big Data Analysis 2
The big data analysis system should be powerful enough to be able to work with the data iteratively. This means the processes can keep looping until the desired result is created. The system must also be able to handle a lot of attributes because big data comes in large sizes with hundreds or even thousands of attribute types in a data source. Whereas before it may have been many records with the same attributes. Big data is often too complicated to make sense of right away in its raw form so the analysis system must be programmatic meaning it can process the data through programs. It is advised that the system should be able to connect to other computer systems via the cloud. This is because if several computers can cooperate to process the same big data set then it will reduce the processing time.
Wednesday, May 29, 2019
Types of Problem suited to Big Data Analysis
predictive analytics
Predictive analytics is when we use big data to look for patterns which can help us predict what will happen in the future based on the pattern found. For example companies and investors can use big data analytics to predict what prices will be in the future from looking at past information of what sales have been in past years. This can help them in business decision making as they can make an estimate of what will likely happen.
diagnostic analytics
Diagnostic analytics is similar to predictive analytics in that it predicts a value, however it does not necessarily predict future values. It can have any 2 variables which can be compared. Using the existing information on the graph, the location of an unmapped value on the graph can be predicted by estimating where it would most likely be. For example the number of sales of hand held fans in a shop could be based on the temperature of that day. Using the information from previous sales and temperatures we can estimate how many fans are likely to be sold based on the temperature of the day. If the temperature is 5 degrees you may expect 1 sale. If the temperature is 30 degrees you might expect 400 sales.
descriptive analytics
Descriptive analytics is when we take data and map it onto a graph. Then see if there is any relationship between two variables. If a relationship is found we say what we think the relationship is. For example if we look at the previous example we can say that the warmer the weather is on a particular day, the more fans will be sold in the shop.
prescriptive analytics
Prescriptive analytics is when we look at the relationship and make decisions based on the relationship and what we can predict. It can be used to create the best outcome of something because we can make predictions based on the relationship. Businesses can use prescriptive analytics in order to maximise profits. Healthcare professionals can use it to give people the most effective treatment. Looking back to the shop example above we can make the decision that the business should buy less hand held fans for their stock during the cold winter and make sure there is a lot of fans stocked in the summer months to maximise profits.
Predictive analytics is when we use big data to look for patterns which can help us predict what will happen in the future based on the pattern found. For example companies and investors can use big data analytics to predict what prices will be in the future from looking at past information of what sales have been in past years. This can help them in business decision making as they can make an estimate of what will likely happen.
diagnostic analytics
Diagnostic analytics is similar to predictive analytics in that it predicts a value, however it does not necessarily predict future values. It can have any 2 variables which can be compared. Using the existing information on the graph, the location of an unmapped value on the graph can be predicted by estimating where it would most likely be. For example the number of sales of hand held fans in a shop could be based on the temperature of that day. Using the information from previous sales and temperatures we can estimate how many fans are likely to be sold based on the temperature of the day. If the temperature is 5 degrees you may expect 1 sale. If the temperature is 30 degrees you might expect 400 sales.
descriptive analytics
Descriptive analytics is when we take data and map it onto a graph. Then see if there is any relationship between two variables. If a relationship is found we say what we think the relationship is. For example if we look at the previous example we can say that the warmer the weather is on a particular day, the more fans will be sold in the shop.
prescriptive analytics
Prescriptive analytics is when we look at the relationship and make decisions based on the relationship and what we can predict. It can be used to create the best outcome of something because we can make predictions based on the relationship. Businesses can use prescriptive analytics in order to maximise profits. Healthcare professionals can use it to give people the most effective treatment. Looking back to the shop example above we can make the decision that the business should buy less hand held fans for their stock during the cold winter and make sure there is a lot of fans stocked in the summer months to maximise profits.
Business example
An example of a company using Big Data for business is Netfix. They have over 100,000,000 subscribers so they have a large pool of data to analyse.
Netflix uses data from each user to show each user their own recommended shows. They do this by looking at the user's previous watch history and their search history to help predict what the user may be interested in, therefore increasing the likelihood that the user will watch the recommended content. This will increase Netflix's profit as it is more likely that more people will be watching for longer because they have been recommended shows they will like.
Netflix uses data from each user to show each user their own recommended shows. They do this by looking at the user's previous watch history and their search history to help predict what the user may be interested in, therefore increasing the likelihood that the user will watch the recommended content. This will increase Netflix's profit as it is more likely that more people will be watching for longer because they have been recommended shows they will like.
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