Advanced Topics in information systems

It is the procedure to find patterns and necessary details from huge amount of data collected from various sources for a period of time. Data mining. Web content-mining. Web structure-mining. Web usage-mining.

It is the process finding patterns- models or knowledge from the contents of a web page. Data mining. Web content-mining. Web structure-mining. Web usage-mining.

It is the process of recognizing the underlying correlations among the web pages and other online objects. Data mining. Web content-mining. Web structure-mining. Web usage-mining.

It is the process of mining browsing patterns from the usage information of the customers. Data mining. Web content-mining. Web structure-mining. Web usage-mining.

Build a function from an input set to class label. Discriminative classifiers. Generative classifiers. Customer information. How would they know the things they are buying are of good quality and whether they serve well?.

Build a model of a joint probability and predict the class label of an input instance using Bayes rules. Discriminative classifiers. Generative classifiers. Customer information. Customer information.

It refers to the personal data of the customers- commodity information refers to the product features such as price amount left etc. and server information refers to the cookies- logs generated by a user session. Discriminative classifiers. Generative classifiers. Customer information. How would they know the things they are buying are of good quality and whether they serve well?.

This problem can be solved using Data Collection Preprocessing and then Classification. Discriminative classifiers. Generative classifiers. Customer information. How would they know the things they are buying are of good quality and whether they serve well?.

In this Phase- the crawler module to collect data through 10 information given . Training phase. Data Preprocessing - Filtering. Classification. J48 Decision Tree.

Outliers and extreme values using weka. Training phase. Data Preprocessing - Filtering. Classification. J48 Decision Tree.

Determine the most significant attribute. Training phase. Data Preprocessing - Filtering. Classification. J48 Decision Tree.

It is a discriminative classifier. Training phase. Data Preprocessing - Filtering. Classification. J48 Decision Tree.

It is a simple probabilistic classifier to predict the probabilities of class labels. Naive Bayes. Multilayer Perception. Python SciPy. print(dataset.describe()).

It is an implementation of Neural Network which has two non-linear activation functions each of which maps weighted inputs to the output of each neuron. Naive Bayes. Multilayer Perception. Python SciPy. print(dataset.describe()).

It is the most useful package for machine learning in Python. Naive Bayes. Multilayer Perception. Python SciPy. print(dataset.describe()).

Show all of the numerical values that have the same scale (Statistical Summary). Naive Bayes. Multilayer Perception. Python SciPy. print(dataset.describe()).

Show each class that has the same number of instances (class distribution). print(dataset.groupby(-class-).size()). Data Visualization: Univariate Plots. Data Visualization: Multivariate Plots. Principal component analysis (PCA).

box- whisker- and histograms plots. print(dataset.groupby(-class-).size()). Data Visualization: Univariate Plots. Data Visualization: Multivariate Plots. Principal component analysis (PCA).

scatter plot matrix. print(dataset.groupby(-class-).size()). Data Visualization: Univariate Plots. Data Visualization: Multivariate Plots. Principal component analysis (PCA).

It is to reduce the dimensionality. print(dataset.groupby(-class-).size()). Data Visualization: Univariate Plots. Data Visualization: Multivariate Plots. Principal component analysis (PCA).

reducing a lot of the dimensionality of the data set. Principal component analysis (PCA). SMOTEBoost technique. Equal Width binning. Normalization.

Can be used in the imbalanced data. Principal component analysis (PCA). SMOTEBoost technique. Equal Width binning. Normalization.

Can be used in search dynamically for the optimal width and number of bins for the target class. Principal component analysis (PCA). SMOTEBoost technique. Equal Width binning. Normalization.

Can be used in data set consists of attributes with different scales and units. Principal component analysis (PCA). SMOTEBoost technique. Equal Width binning. Normalization.

Can be used in learning from each instance from other known outputs of the data set. K-Nearest Neighbors (K-NN). Euclidian distance. Recommender systems. An Example of Recommender systems.

Can be used in measuring the similarity metric amongst neighbors. K-Nearest Neighbors (K-NN). Euclidian distance. Recommender systems. An Example of Recommender systems.

These basic models to content-based and collaborative filtering. K-Nearest Neighbors (K-NN). Euclidian distance. Recommender systems. An Example of Recommender systems.

YouTube uses it to decide which video to play next on auto play. K-Nearest Neighbors (K-NN). Euclidian distance. Recommender systems. An Example of Recommender systems.

They are based on popularity or average audience. Simple recommenders. Content-based recommenders. Collaborative filtering recommenders. The first step of recommendation function.

They are based on a particular item metadata. Simple recommenders. Content-based recommenders. Collaborative filtering recommenders. The first step of recommendation function.

They are based preference that a user would give an itembased on past ratings and preferences of other users. Simple recommenders. Content-based recommenders. Collaborative filtering recommenders. The first step of recommendation function.

Get the index. Simple recommenders. Content-based recommenders. Collaborative filtering recommenders. The first step of recommendation function.

Get the list of cosine similarity scores. The second step of recommendation function. The third step of recommendation function. The fourth step of recommendation function. Null-based Completeness (NBC).

Sort the list of tuples. The second step of recommendation function. The third step of recommendation function. The fourth step of recommendation function. Null-based Completeness (NBC).

Get the top 10 elements of this list. The second step of recommendation function. The third step of recommendation function. The fourth step of recommendation function. Null-based Completeness (NBC).

It Measures the values that are missing- normally presented as nulls. The second step of recommendation function. The third step of recommendation function. The fourth step of recommendation function. Null-based Completeness (NBC).

It measures the tuples or records that are missing. Tuple-based Completeness(TBC). Schema-based Completeness (SBC). Population-based Completeness (PBC). Integrated database.

It measures the missing schema elements like attribute and entities. Tuple-based Completeness(TBC). Schema-based Completeness (SBC). Population-based Completeness (PBC). Integrated database.

It measures the missing individuals from datasets under measure. Tuple-based Completeness(TBC). Schema-based Completeness (SBC). Population-based Completeness (PBC). Integrated database.

Can be used where the participants must agree to a unified view of data structure that is transparent to all. Tuple-based Completeness(TBC). Schema-based Completeness (SBC). Population-based Completeness (PBC). Integrated database.

It is the problem of studying an agent in an environment- the agent has to interact with the environment in order to maximize some cumulative rewards. Reinforcement Learning. Markov Decision Process (MDP). Optimal Value Functions and Policy. Dynamic Programming.

It is a collection of States- Actions- Transition Probabilities Rewards- Discount Factor: (S- A- P- R- ?). Reinforcement Learning. Markov Decision Process (MDP). Optimal Value Functions and Policy. Dynamic Programming.

It is a function that gives the maximum value at each state among all policies. Reinforcement Learning. Markov Decision Process (MDP). Optimal Value Functions and Policy. Dynamic Programming.

It is mainly an optimization over plain recursion. Reinforcement Learning. Markov Decision Process (MDP). Optimal Value Functions and Policy. Dynamic Programming.

Can be used to solve the lack of delivery of goods to a distant place or a relatively long time and high cost of providing the purchased product hinders further development of e-commerce. Cooperation between online shops dealing with cross-border trade. Petri net. Funnel analysis. Process mining.

It is one of several mathematical modeling languages for the description of distributed systems. It is a class of discrete event dynamic system. Cooperation between online shops dealing with crossborder trade. Petri net. Funnel analysis. Process mining.

It is the mapping and analysis of a series of events that lead towards a defined goal- like completing the sign-up or making a purchase (understand user behavior). Cooperation between online shops dealing with cross-border trade. Petri net. Funnel analysis. Process mining.

It is a set of techniques used for obtaining knowledge of and extracting insights from processes by the means of analyzing the event data- generated during the execution of the process. Cooperation between online shops dealing with cross-border trade. Petri net. Funnel analysis. Process mining.

It is converting an event log into a process model. Process discovery. Conformance checking. throughput analysis/bottleneck detection. pm4py library.

It is investigating the differences between the model and what happens in real life. Process discovery. Conformance checking. throughput analysis/bottleneck detection. pm4py library.

It is accounting for the intensity of events’ execution (measured by time spent to complete a particular event). Process discovery. Conformance checking. throughput analysis/bottleneck detection. pm4py library.

It is used to process discovery algorithms in Python. Process discovery. Conformance checking. throughput analysis/bottleneck detection. pm4py library.

It is the algorithm scans the traces (sequences in the event log) for ordering relations and builds the footprint matrix. Alpha Miner. Heuristic Miner. Inductive Miner. An experience economy.

It is an improvement of the Alpha Miner algorithm and acts on the Directly-Follows Graph. It can be converted into a Petri net. Alpha Miner. Heuristic Miner. Inductive Miner. An experience economy.

It is an improvement of both the Alpha Miner and Heuristics Miner. Alpha Miner. Heuristic Miner. Inductive Miner. An experience economy.

They need to create memorable events—“the experience”—and that is what customers are willing to pay for. Alpha Miner. Heuristic Miner. Inductive Miner. An experience economy.

It is a process of delivering to each customer the right product- in the right place and at the right time. Personalization. The model that employs process mining- recommender systems- and big data analysis. Behavioral data Profile. Demographic data Profile.

Can be used to solve the breakthrough concerns the way enormous volumes of data concerning customers can be gathered and analyzed . Personalization. The model that employs process mining- recommender systems- and big data analysis. Behavioral data Profile. Demographic data Profile.

purchase history (products or services purchased by the customer); products viewed but not purchased; products added to the cart but eventually abandoned; products being searched for;. Personalization. The model that employs process mining- recommender systems- and big data analysis. Behavioral data Profile. Demographic data Profile.

Age- gender- residential area (address)- education occupation;. Personalization. The model that employs process mining- recommender systems- and big data analysis. Behavioral data Profile. Demographic data Profile.

Interests (movies- music- books- hobbies)- friends. Social profile. Social media profile. Lifestyle data Profile. Family details Profile.

Activities on social media (e.g. Facebook likes and dislikes Twitter followings- etc.);. Social profile. Social media profile. Lifestyle data Profile. Family details Profile.

Type of property owned- pets;. Social profile. Social media profile. Lifestyle data Profile. Family details Profile.

Marital status- children;. Social profile. Social media profile. Lifestyle data Profile. Family details Profile.

e.g. smartphone brand;. Device-related data Profile. Psychographics Profile. Personal wishes Profile. Contextual data Profile.

Religious and political views;. Device-related data Profile. Psychographics Profile. Personal wishes Profile. Contextual data Profile.

Expectations and interests expressed directly by the customer;. Device-related data Profile. Psychographics Profile. Personal wishes Profile. Contextual data Profile.

e.g. customer’s location-related data such as current weather or social events being held. Device-related data Profile. Psychographics Profile. Personal wishes Profile. Contextual data Profile.

Color- lighting level- appearance of objects (size and shape). Visual atmospheric dimension such as. Aural atmospheric dimension such as. Olfactory atmospheric dimension such as. Tactile atmospheric dimension such as.

Volume- pitch- tempo and style of sounds. Visual atmospheric dimension such as. Aural atmospheric dimension such as. Olfactory atmospheric dimension such as. Tactile atmospheric dimension such as.

Nature and intensity of sound. Visual atmospheric dimension such as. Aural atmospheric dimension such as. Olfactory atmospheric dimension such as. Tactile atmospheric dimension such as.

Temperature- texture and contact. Visual atmospheric dimension such as. Aural atmospheric dimension such as. Olfactory atmospheric dimension such as. Tactile atmospheric dimension such as.

Nature and intensity of taste sensations. Taste atmospheric dimension such as. Aural atmospheric dimension such as. Olfactory atmospheric dimension such as. Tactile atmospheric dimension such as.

It can solve Complicated calculation for image processing and the inability to control all places- due to a limited image. Taste atmospheric dimension such as. Historical data in repeatedly visited areas. Olfactory atmospheric dimension such as. Tactile atmospheric dimension such as.

It is the procedure to find patterns and necessary details from huge amount of data collected from various sources for a period of time. Means That Data mining. TRUE. FALSE.

It is the process finding patterns- models or knowledge from the contents of a web page. Means That Web content-mining. TRUE. FALSE.

It is the process of recognizing the underlying correlations among the web pages and other online objects. Means That Web structure-mining. TRUE. FALSE.

It is the process of mining browsing patterns from the usage information of the customers Means That Web usage-mining. TRUE. FALSE.

Build a function from an input set to class label. Means That Web usage-mining. TRUE. FALSE.

Build a model of a joint probability and predict the class label of an input instance using Bayes rules Means That Generative classifiers. TRUE. FALSE.

It refers to the personal data of the customers- commodity information refers to the product features such as priceamount left etc. and server information refers to the cookies- logs generated by a user session. Means That Generative classifiers. TRUE. FALSE.

This problem can be solved using Data CollectionPreprocessing and then Classification. Means That Customer information. TRUE. FALSE.

In this Phase- the crawler module to collect data through 10 information given . Means That Training phase. TRUE. FALSE.

Outliers and extreme values using weka Means That Data Preprocessing - Filtering. TRUE. FALSE.

Determine the most significant attribute Means That Data Preprocessing - Filtering. TRUE. FALSE.

It is a discriminative classifier. Means That Classification. TRUE. FALSE.

It is a simple probabilistic classifier to predict the probabilities of class labels. Means That Naive Bayes. TRUE. FALSE.

It is an implementation of Neural Network which has two non-linear activation functions each of which maps weighted inputs to the output of each neuron. Means That Naive Bayes. TRUE. FALSE.

It is the most useful package for machine learning in Python. Means That Multilayer Perception. TRUE. FALSE.

Show all of the numerical values that have the same scale (Statistical Summary) Means That Python SciPy. TRUE. FALSE.

Show each class that has the same number of instances (class distribution) Means That print(dataset.describe()). TRUE. FALSE.

box- whisker- and histograms plots Means That Data Visualization: Univariate Plots. TRUE. FALSE.

scatter plot matrix Means That Data Visualization: Univariate Plots. TRUE. FALSE.

It is to reduce the dimensionality Means That Principal component analysis (PCA). TRUE. FALSE.

reducing a lot of the dimensionality of the data set. Means That Principal component analysis (PCA). TRUE. FALSE.

Can be used in the imbalanced data. Means That Principal component analysis (PCA). TRUE. FALSE.

Can be used in search dynamically for the optimal width and number of bins for the target class. Means That SMOTEBoost technique. TRUE. FALSE.

Can be used in data set consists of attributes with different scales and units. Means That Normalization. TRUE. FALSE.

Can be used in learning from each instance from other known outputs of the data set. Means That K-Nearest Neighbors (K-NN). TRUE. FALSE.

Can be used in measuring the similarity metric amongst neighbors. Means That Euclidian distance. TRUE. FALSE.

These basic models to content-based and collaborative filtering Means That Euclidian distance. TRUE. FALSE.

YouTube uses it to decide which video to play next on auto play. Means That Recommender systems. TRUE. FALSE.

They are based on popularity or average audience. Means That An Example of Recommender systems. TRUE. FALSE.

They are based on a particular item metadata. Means That Content-based recommenders. TRUE. FALSE.

They are based preference that a user would give an itembased on past ratings and preferences of other users. Means That Content-based recommenders. TRUE. FALSE.

Get the index. Means That Collaborative filtering recommenders. TRUE. FALSE.

Get the list of cosine similarity scores Means That The frist step of recommendation function. TRUE. FALSE.

Sort the list of tuples Means That The third step of recommendation function. TRUE. FALSE.

Get the top 10 elements of this list. Means That The third step of recommendation function. TRUE. FALSE.

It Measures the values that are missing- normally presented as nulls Means That The fourth step of recommendation function. TRUE. FALSE.

It measures the tuples or records that are missing. Means That Tuple-based Completeness(TBC). TRUE. FALSE.

It measures the missing schema elements like attribute and entities. Means That Schema-based Completeness (SBC). TRUE. FALSE.

It measures the missing individuals from datasets under measure. Means That Schema-based Completeness (SBC). TRUE. FALSE.

Can be used where the participants must agree to a unified view of data structure that is transparent to all Means That Population-based Completeness (PBC). TRUE. FALSE.

It is the problem of studying an agent in an environment- the agent has to interact with the environment in order to maximize some cumulative rewards. Means That Reinforcement Learning. TRUE. FALSE.

It is a collection of States- Actions- Transition Probabilities Rewards- Discount Factor: (S- A- P- R- ?) Means That Reinforcement Learning. TRUE. FALSE.

It is a function that gives the maximum value at each state among all policies Means That Optimal Value Functions and Policy. TRUE. FALSE.

It is mainly an optimization over plain recursion. Means That Dynamic Programming. TRUE. FALSE.

Can be used to solve the lack of delivery of goods to a distant place or a relatively long time and high cost of providing the purchased product hinders further development of e-commerce. Means That Dynamic Programming. TRUE. FALSE.

It is one of several mathematical modeling languages for the description of distributed systems. It is a class of discrete event dynamic system. Means That Cooperation between online shops dealing with cross-border trade. TRUE. FALSE.

It is the mapping and analysis of a series of events that lead towards a defined goal- like completing the sign-up or making a purchase (understand user behavior). Means That Petri net. TRUE. FALSE.

It is a set of techniques used for obtaining knowledge of and extracting insights from processes by the means of analyzing the event data- generated during the execution of the process. Means That Funnel analysis. TRUE. FALSE.

It is converting an event log into a process model. Means That Process discovery. TRUE. FALSE.

It is investigating the differences between the model and what happens in real life. Means That Conformance checking. TRUE. FALSE.

It is accounting for the intensity of events’ execution (measured by time spent to complete a particular event). Means That throughput analysis/bottleneck detection. TRUE. FALSE.

It is used to process discovery algorithms in Python Means That pm4py library. TRUE. FALSE.

It is the algorithm scans the traces (sequences in the event log) for ordering relations and builds the footprint matrix. Means That Alpha Miner. TRUE. FALSE.

It is an improvement of the Alpha Miner algorithm and acts on the Directly-Follows Graph. It can be converted into a Petri net. Means That Heuristic Miner. TRUE. FALSE.

It is an improvement of both the Alpha Miner and Heuristics Miner. Means That Inductive Miner. TRUE. FALSE.

They need to create memorable events—“the experience”—and that is what customers are willing to pay for Means That Inductive Miner. TRUE. FALSE.

It is a process of delivering to each customer the right product- in the right place and at the right time. Means That Personalization. TRUE. FALSE.

Can be used to solve the breakthrough concerns the way enormous volumes of data concerning customers can be gathered and analyzed . Means That The model that employs process mining- recommender systems- and big data analysis. TRUE. FALSE.

purchase history (products or services purchased by the customer); products viewed but not purchased; products added to the cart but eventually abandoned; products being searched for; Means That Behavioral data Profile. TRUE. FALSE.

Age- gender- residential area (address)- education occupation; Means That Demographic data Profile. TRUE. FALSE.

Interests (movies- music- books- hobbies)- friends Means That Demographic data Profile. TRUE. FALSE.

Activities on social media (e.g. Facebook likes and dislikesTwitter followings- etc.); Means That Social media profile. TRUE. FALSE.

Type of property owned- pets; Means That Lifestyle data Profile. TRUE. FALSE.

Marital status- children; Means That Family details Profile. TRUE. FALSE.

e.g. smartphone brand; Means That Device-related data Profile. TRUE. FALSE.

Religious and political views; Means That Psychographics Profile. TRUE. FALSE.

Expectations and interests expressed directly by the customer; Means That Personal wishes Profile. TRUE. FALSE.

e.g. customer’s location-related data such as current weather or social events being held. Means That Personal wishes Profile. TRUE. FALSE.

Color- lighting level- appearance of objects (size and shape) Means That Contextual data Profile. TRUE. FALSE.

Volume- pitch- tempo and style of sounds Means That Aural atmospheric dimension such as. TRUE. FALSE.

Nature and intensity of sound Means That Aural atmospheric dimension such as. TRUE. FALSE.

Temperature- texture and contact Means That Olfactory atmospheric dimension such as. TRUE. FALSE.

Nature and intensity of taste sensations Means That Taste atmospheric dimension such as. TRUE. FALSE.

t can solve Complicated calculation for image processing and the inability to control all places- due to a limited image Means That Historical data in repeatedly visited areas. TRUE. FALSE.