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One month later in early 2009, Oliver Lamming, a colleague of mine, mentioned that the CUE3 business plan competition was coming up and asked if I had any ideas. We would be competing for a 1k prize and entry to the 5k final. I pulled out my iPhone and checked the calendar to see how many weeks we had to decide. Suddenly, the penny dropped and it all became clear: we would build a Sudoku solver for the iPhone and sell it on the App Store! Oliver and I had the necessary technical skills, but no business savvy or marketing experience. We would need someone to fill that gap. The two of us began attending Enterprise Tuesdays, a popular series of talks for would-be entrepreneurs, held at the Engineering Department in Cambridge. After the main talks, there were networking sessions. It was in one of those sessions where we met Emmanuel Carraud, an MBA student with a decade of international marketing experience under his belt. Emmanuel got fired up about our idea and soon team iSolve was formed.

The Robosapien robot is by all accounts something that is greater than the sum of its parts: a remarkable toy that combines high tech with low tech, complexity with simplicity, and humanistic traits with robotics. It is amazing to learn all that went into creating this robot. Let s start our journey by looking at Robosapien s creator, Mark Tilden, and some of his ideas that have revolutionized not only the toy industry but theories of robotics as well.

Note Although the Oracle data dictionary consists of public synonyms, based on views, based on underlying (undocumented) tables, we sometimes just refer to data dictionary tables for convenience.

Cambridge University Entrepreneurs. A society that provides training and advice to students on starting up a company.

(Photo courtesy of WowWee Ltd.)

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This appendix starts with some commonly used data dictionary views, and then lists views organized by the four separate view categories, which you can recognize by their prefix: ALL views have information about all accessible data. Regular database users have access to these views. USER views have information about your own data. Regular database users have access to these views. DBA views have database-wide information. You need DBA privileges (or some specific system privileges) to access these views. V$ views are dynamic performance views. You need DBA privileges (or some specific system privileges) to access these views. The first three categories are commonly referred to as static to distinguish them from the fourth category, the dynamic performance views. This appendix lists the names of the data dictionary views, without any column descriptions. For column details, you can use the SQL*Plus DESCRIBE command or query the DICT_COLUMNS data dictionary view (see Listing 3-11 in 3 of this book). The view descriptions themselves are also very concise, and they may refer to concepts or terms not covered anywhere in this book. See Oracle Reference for further data dictionary details and explanations.

After writing and submitting our executive summary and app development plan, iSolve won the 1k prize. This money was immediately invested into purchasing two Mac Minis so that we could begin developing the app. At the awards ceremony, we met some graduates who developed a similar application in the past and were also involved in Computer Vision research. They provided us with advice and expertise for the next round. The 5k grand finale would involve a more detailed business plan, a live demo, and a pitch to future investors (Dragon s Den style, see Figure 5 2). To make things more difficult, the finale was just days before our final exams.4 Despite a tough pitch and technical problems with the demo, we pulled through and seized the 5k prize. This led to the incorporation of our company MagicSolver.com Ltd.

Table B-1 lists some commonly used data dictionary views. Table B-1. Commonly Used Data Dictionary Views

n this chapter we ll look into the background of Robosapien: its creator, Mark Tilden (shown in Figure 2-1), and his theories of BEAM robotics, on which Robosapien is loosely based. We ll close the chapter by looking at some of the various types of Robosapiens on the market, and the awards and accolades that Robosapien has received.

Figure 5 2. MagicSolver.com pitching to a panel of judges and investors at the CUE 5k Grand Finale.

DICTIONARY (or DICT) DICT_COLUMNS DUAL FLASHBACK_TRANSACTION_QUERY NLS_DATABASE_PARAMETERS NLS_INSTANCE_PARAMETERS NLS_SESSION_PARAMETERS PUBLICSYN ROLE_ROLE_PRIVS ROLE_SYS_PRIVS ROLE_TAB_PRIVS SESSION_PRIVS SESSION_ROLES

Description of all data dictionary tables Description of all data dictionary table columns Dummy table with a single row and a single column All flashback transaction queries in the database NLS (National Language Support) parameter settings for the database NLS parameter settings for the instance NLS parameter settings for your current session All public synonyms in the database Roles granted to roles System privileges granted to roles Table privileges granted to roles Active session privileges Active session roles

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