Creating and working with databases in Android

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The document discusses code for a TODOs application that uses an SQLite database. It covers creating the database adapter and helper classes, writing methods to insert, update, and delete TODO items from the database, and retrieving data. It also discusses running the application and viewing TODO items, as well as the code for an activity to edit an individual TODO item.

Technology

A database example Running the application TODOs application code Inspecting the database Using LogCat

CS/SE Individual Practical

Stephen Gilmore
October 28, 2011
School of Informatics, University of Edinburgh

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A database example Running the application TODOs application code Inspecting the database Using LogCat
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Lars Vogel example: TODOs

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A database example

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A database example

TodoDatabaseAdapter

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TodoDatabaseHelper (onCreate())

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The create, update, and delete methods

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The insert() method

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The update() method

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The delete() method

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Fetch data

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Create content values

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Resources

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Running the application

Running the TODOs application

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Running the application

Editing a TODO item

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Running the application

Setting category to “Urgent”

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TODOs application code

TodoDetails (imports)

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Graphical layout of todo edit.xml

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Text of todo edit.xml

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Outline of todo edit.xml

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Save state, onPause, onResume

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Save state

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TodosOverview (onCreate)

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Options menu, item selected

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Options item selected

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TODOs application code

Accessing the insert menu

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Inspecting the database

Dalvik Debug Monitor Server (DDMS)

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File explorer in DDMS

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/data/data/de.vogella.android.todos/...

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Pulling a ﬁle from the device

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Get the device ﬁle

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Inspecting the ﬁle with sqlite3

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Using LogCat

Debugs and errors displayed in LogCat

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Using LogCat

Can ﬁlter messages displayed in LogCat

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Using LogCat

Can use view menu to export messages

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Smarter Aviation Data Management: Lessons from Swedavia Airports and SwecoSafe Software

Managing airport and airspace data is no small task, especially when you’re expected to deliver it in AIXM format without spending a fortune on specialized tools. But what if there was a smarter, more affordable way? Join us for a behind-the-scenes look at how Sweco partnered with Swedavia, the Swedish airport operator, to solve this challenge using FME and Esri. Learn how they built automated workflows to manage periodic updates, merge airspace data, and support data extracts – all while meeting strict government reporting requirements to the Civil Aviation Administration of Sweden. Even better? Swedavia built custom services and applications that use the FME Flow REST API to trigger jobs and retrieve results – streamlining tasks like securing the quality of new surveyor data, creating permdelta and baseline representations in the AIS schema, and generating AIXM extracts from their AIS data. To conclude, FME expert Dean Hintz will walk through a GeoBorders reading workflow and highlight recent enhancements to FME’s AIXM (Aeronautical Information Exchange Model) processing and interpretation capabilities. Discover how airports like Swedavia are harnessing the power of FME to simplify aviation data management, and how you can too.

The Future of Product Management in AI ERA.pdfAlyona Owens

Hi, I’m Aly Owens, I have a special pleasure to stand here as over a decade ago I graduated from CityU as an international student with an MBA program. I enjoyed the diversity of the school, ability to work and study, the network that came with being here, and of course the price tag for students here has always been more affordable than most around. Since then I have worked for major corporations like T-Mobile and Microsoft and many more, and I have founded a startup. I've also been teaching product management to ensure my students save time and money to get to the same level as me faster avoiding popular mistakes. Today as I’ve transitioned to teaching and focusing on the startup, I hear everybody being concerned about Ai stealing their jobs… We’ll talk about it shortly. But before that, I want to take you back to 1997. One of my favorite movies is “Fifth Element”. It wowed me with futuristic predictions when I was a kid and I’m impressed by the number of these predictions that have already come true. Self-driving cars, video calls and smart TV, personalized ads and identity scanning. Sci-fi movies and books gave us many ideas and some are being implemented as we speak. But we often get ahead of ourselves: Flying cars,Colonized planets, Human-like AI: not yet, Time travel, Mind-machine neural interfaces for everyone: Only in experimental stages (e.g. Neuralink). Cyberpunk dystopias: Some vibes (neon signs + inequality + surveillance), but not total dystopia (thankfully). On the bright side, we predict that the working hours should drop as Ai becomes our helper and there shouldn’t be a need to work 8 hours/day. Nobody knows for sure but we can require that from legislation. Instead of waiting to see what the government and billionaires come up with, I say we should design our own future. So, we as humans, when we don’t know something - fear takes over. The same thing happened during the industrial revolution. In the Industrial Era, machines didn’t steal jobs—they transformed them but people were scared about their jobs. The AI era is making similar changes except it feels like robots will take the center stage instead of a human. First off, even when it comes to the hottest space in the military - drones, Ai does a fraction of work. AI algorithms enable real-time decision-making, obstacle avoidance, and mission optimization making drones far more autonomous and capable than traditional remote-controlled aircraft. Key technologies include computer vision for object detection, GPS-enhanced navigation, and neural networks for learning and adaptation. But guess what? There are only 2 companies right now that utilize Ai in drones to make autonomous decisions - Skydio and DJI.

The Growing Value and Application of FME & GenAISafe Software

With the cost of using Generative AI services dropping exponentially and the array of available models continually expanding, integrating AI into FME workflows has become inexpensive, accessible and effective. This presentation explores how GenAI within FME can cost-effectively transform data workflows by automating data extraction, validation, classification and augmentation tasks. We’ll discuss how FME’s no-code flexibility enables users to combine Generative AI and Computer Vision tools that create efficient workflows tailored to specific challenges. Using recent practical examples, we’ll demonstrate how these integrations can simplify complex tasks, save time and enhance data quality.

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