Core Data on iOS 5 Tutorial: How To Work with Relations and Predicates | badz.info
Q. Can I use more than one entity type in a single SQLite database file? to different tables in SQLite) and relationships (references) between the entities that then form the runtime object graph. A. You use an NSPredicate with an NSFetchRequest. Quiz 1. What is the application sandbox? 2. What four directories in the. So today, I'm chatting about the power of NSPredicate and how you But as the requirements become more complex and the relationships more varied, the code gets a bit iffy. It really got its bones when paired with Core Data. .. iOS 12 beckons, but before it does - let's quiz it up over iOS versions of. We've already worked with relationships in the Core Data model editor .. but predicates are what really makes fetching powerful in Core Data.
Furthermore, Sherlock also provides a generic framework for generating quizzes of multiple domains with minimum human effort, and its effectiveness has been evaluated on datasets from three different domains.
The rest of the paper is organised as follows. Related Work Games with a Purpose and Educational Games A series of symmetric and asymmetric verification games was presented in [ 26 ] with the aim to motivate humans to contribute to building the Semantic Web. Other quiz-like games [ 3132 ] focus on ranking, rating and cleansing linked data. The assumption underlying these games is that the frequency of a question being correctly answered implies the importance of the supporting linked data used to create the quiz.
However, the focus of these games is to harness human intelligence to perform tasks that cannot be automated, rather than creating learning experiences for humans. In contrast to games with a purpose, Damljanovic et al. LDMQ is able to generate quizzes related to a user-selected actor or actress, asking questions about the director, the release date or the characters of a film in which the actor or actress has appeared.
The question and correct answers are directly derived from the results of SPARQL 3 queries against the Linked Movie Data Base LMDB [ 12 ], whereas the incorrect answers are randomly chosen from a set of candidates collected following some handcrafted rules. One of the common limitations shared by existing quiz generation systems is the domain-dependent issue.
That is when applying the template-based quiz generation method to a new domain, significant human efforts must be required on tasks such as creating new question templates, writing SPARQL queries according to a domain-specific ontology and defining rules for collecting wrong answers for a quiz.
Again, these tasks are not trivial for non-domain experts such as teachers, content editors and mainstream web users.
Sherlock: A Semi-automatic Framework for Quiz Generation Using a Hybrid Semantic Similarity Measure
In addition, most of the existing quiz generation systems endeavour to automate the quiz creation task to the largest extent without providing the functionality for manual quiz creation. However, allowing manual question authoring from end-users is important because it can increase both the level of user engagement and topic diversity of the generated quizzes.
Moreover, creating quizzes offers the creator the opportunity of teaching someone else, which is the lowest level of the Learning Pyramid. Finally, quizzes with varying difficulty levels are important for formal learning. This has in turn motivated us to develop a systematic way of measuring quiz difficulty level using semantic similarity measures.
Similarity Measures A similarity distance measure reflects the degree of closeness or separation of the target objects, and it must be determined before performing clustering.
In this work, we tackle the research challenge of how to predict the difficulty levels of quizzes perceived by humans in terms of similarity measures, which to our knowledge, has not been studied in previous work. Therefore, we review some of the most representative similarity measures in the literature, which serve as the ground for our preliminary experiments.
Sherlock: A Semi-automatic Framework for Quiz Generation Using a Hybrid Semantic Similarity Measure
Corpus-Based Approaches Measures of text similarity have been used for a long time in natural language processing applications and related areas. Corpus-based measures aim to identify the degree of similarity between text units using statistical patterns of words derived from large corpora, where the most representative measures are cosine similarity, averaged Kullback—Leibler divergence KLD and the squared Euclidean distance [ 15 ]. Cosine similarity is one of the most popular similarity measures and has been widely used in information retrieval and text clustering applications [ 15 ].
When text documents are represented as term vectors, the similarity of two documents corresponds to the inner product space of the two vectors, i. The averaged Kullback—Leibler divergence KLDrooted from information theory-based clustering, evaluates the differences between two probability distributions.
By modelling a document as a probability distribution over terms, the similarity of two documents is then transformed as the distance between two corresponding probability distributions. Some more advanced approaches rely on word co-occurrence patterns derived from large corpus, which indicate the degree of statistical dependence between text units. Such statistical dependences can then be used for measuring text similarity.
Representative approaches along this line include pointwise mutual information PMI [ 29 ] and latent semantic analysis LSA [ 18 ]. Knowledge-Based Approaches In contrast to corpus-based approaches that are purely oriented on statistical techniques, knowledge-based approaches rely on human-organised knowledge e. WordNet [ 7 ] is a large English lexical knowledge database in which terms are grouped into different sets known as synsets with a list of synonyms. Build and run, add a few banks, and swipe one of the cells to show the delete button.
Also make sure that a XIB is generated. The view will be pushed by a view controller, so you might want to visualize the space taken by a navigation bar.
This will be displayed when necessary via code to edit dates. For the moment, place the picker outside of the visible area of the view. The Y of the picker should be set to With the picker selected, switch to the Size Inspector tab on the right sidebar and set its position as follows: Add it below the existing initWithNibName: The operation in this case is easy: Add the code for it to the end of the file but before the final end: Add it to the end of the file: Add the necessary code again to the end of the file: Add the following code below viewDidLoad: Add the code for those to the end of the file: Before showing the date picker, the first responder for all text fields is resigned, thus effectively dismissing the keyboard if it was visible.
To test your new view, you need to push it onto the navigation stack when a cell is tapped. Run the app and create a few instances of banks. Each bank record is editable, including the close date. To save data, hit the save button; to discard changes, just tap the back button. Notice that the date picker and the keyboard never obstruct each other.
Changes are reflected in the list of banks with no need to refresh the table view. Think of the classic example of employees and departments — an employee is said to belong to a department, and a department has employees.
In database modeling, relationships can be of three types: This property is usually referred to as cardinality. In the example from the previous section, there is already a relation modeled in Core Data: This is a one-to-one relationship: The graphical view stresses this point by connecting the two entities with one single arrow line. In other words, these two only have eyes for each other. This is just a string identifying the name of the relation.
This is the target or the destination class of the relation. The answer to the question: Is the destination a single object or not? If yes, the relation is of type to-one, otherwise it is a to-many. The definition of the inverse function. It is pretty rare to find a domain where this is not needed. It is also a sort of logical necessity: In your example, a department can have more than one employee, so this is a to-many relation.
As a general rule, a one-to-many relation has a many-to-one inverse. In case you want to define a many-to-many relationship, you simply define one relation as to-many and its inverse as a to-many as well. Make sure you define an inverse for each relationship, since Core Data exploits this information to check the consistency of the object graph whenever a change is made.
This defines the behavior of the application when the source object of a relationship is deleted. For the delete rule in 5 above, there are four possible values: Nullify is the simplest option. They just keep thinking they have not been fired: If you select cascade as the delete rule, then when you delete the source object it also deletes the destination object s.
Such a rule is appropriate only if you want to close a department and fire all of its employees as well. In this case it is enough to set the delete rule for department to cascade and delete that department record.
Deny, on the other hand, prevents accidental deletions. Delete rules have to be specified for both sides of a relationship, from employee to department and vice versa.
Each domain implements its own business logic, so there is no general recipe for setting delete rules. Just remember to pay attention when using the cascade rule, since it could result in unexpected consequences.
Core Data on iOS 5 Tutorial: How To Work with Relations and Predicates
To maximize the performance of your application, remember this when you devise your data model and try to use relationships only if necessary. The first step is to add a new entity. The delete rule is the default, nullify 4. As above, this is a to-many relationship 3 with a delete rule of nullify.
A new class, named Tag, will pop up in your project tree.
Sometimes, quite often, in fact: If this happens to you, select one set of instances and delete them, but choose to remove references rather than to trash the files. At this point, you have changed the Core Data model, so your app will not be compatible with the old model on your device. This new view controller will facilitate the creation of new tags and associating them to a bank details object.
Remember to check the box to create the accompanying XIB file. Finally, you add a method to initialize the component with an instance of details.
Add the code for it as follows to the end of the file but before the final end: Replace the existing viewDidLoad with the following: You need this to show a tag as picked by means of a tick in the table view.
You also set up a navigation item to add new tags. Add the following below viewDidLoad: Now add the following code to the end of the file: The code above will display an alert asking the user to insert a new tag: In such a case, instead of implementing the change protocols to the table, you fetch the result again and reload the table view for the sake of simplicity.
YES]; if [pickedTags containsObject: Next implement this callback: At the bottom of the method implementation, add this code: As a final touch, make the label backgrounds gray to show their tappable area.