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Byte Magazine
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Byte Magazine Collection (Issues from 1980 to 1997) |  |
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US $9.98 | 28d 15h 54m |
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Eight Byte magazines - 1979 thru 1988 | |
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US $50.00 | 7h 50m |
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Byte Magazine - Winter Computing - V7 N2 - Feb 1982 | |
0 Bid | US $9.99 | 1d 3h 49m |
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BIX Suspenders. BYTE Magazine's online service BIX. Pre-Internet relic 1988! | |
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US $11.00 | 1d 16h 19m |
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1981 BYTE Magazine Xerox Alto Computer Review - Apple Lisa Macintosh Steve Jobs | |
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US $38.00 | 1d 21h 18m |
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BYTE Magazine October 1995 Vol 20, Number 10 | |
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US $8.00 | 1d 19h 48m |
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BYTE Magazine December 1995 Vol 20, Number 12 | |
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US $8.00 | 1d 19h 50m |
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BYTE Magazine November 1995 Vol 20, Number 11 | |
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US $8.00 | 1d 19h 49m |
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BYTE Magazine Vol 2 # 9 September 1977 SCORTOS Implementation Music Language Mag | |
0 Bid | US $9.99 | 1d 23h 37m |
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Vintage BYTE Magazine 1981 VOL 6 # 1 2 3 4 5 6 7 9 10 11 12 Lot | |
0 Bid | US $45.99 | 1d 23h 39m |
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BYTE MAGAZINE - Vintage Computer Smarts From 1979 - 64KB Like Wowee | |
0 Bid | US $3.95 | 1d 22h 29m |
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2 Byte magazine vol. 10 no. 11 1985 and May 1988 | |
0 Bid | US $.98 | 3d 9h 5m |
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Byte Magazine Sept 1977 Vol 2 #9 Music Lang SCORTOS | |
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US $19.99 | 17d 12h 54m |
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Byte Magazine Oct 1977 Vol 2 #10 Space War | |
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US $19.99 | 17d 12h 54m |
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Byte Magazine Aug 1979 Vol 4 #8 LISP | |
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US $19.99 | 17d 12h 47m |
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Byte Magazine May 1984 Vol 9 #5 Computers & Professions | |
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US $9.99 | 17d 12h 53m |
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BYTE magazine February 1980 Computer games PC Floppy Apple IBM | |
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US $18.00 | 28d 12h 38m |
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BYTE magazine April 1984 Computer games PC Floppy Apple IBM | |
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US $18.00 | 28d 12h 35m |
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BYTE magazine January 1980 Computer games PC Floppy Apple IBM | |
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US $18.00 | 28d 12h 32m |
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BYTE magazine June 1983 Computer games PC Floppy Apple IBM 16 Bit | |
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US $12.00 | 28d 12h 36m |
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BYTE magazine December 1979 Computer games PC Floppy Apple IBM | |
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US $18.00 | 28d 12h 34m |
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BYTE magazine June 1979 Computer games PC Floppy Apple IBM | |
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US $18.00 | 28d 12h 39m |
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Byte magazine May - December 1977 . 8 issues !!!!! | |
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US $35.99 | 4d 10h 7m |
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10 Byte magazine 1977 1978 1979. big lot !!!!! | |
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US $35.99 | 4d 10h 10m |
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Byte Magazine - Computers & Space - V10 N7 - July 1985 | |
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US $11.04 | 27d 14h 11m |
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Byte Magazine - Programming Tech - V10 N6 - June 1985 | |
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US $11.04 | 27d 14h 13m |
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Byte Magazine First Issue #1 September 1975 Vintage Computing Computers | |
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US $199.99 | 23d 21h 16m |
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Byte Magazine Issue #16 December 1976 Vintage Computing Computers | |
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US $19.99 | 23d 21h 43m |
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Byte Magazine Issue #14 October 1976 Vintage Computing Computers Morse Code | |
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US $24.99 | 23d 21h 38m |
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Byte Magazine Issue #12 August 1976 Vintage Computing Computers Zilog Z80 | |
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US $19.99 | 23d 21h 32m |
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Byte Magazine Issue #6 February 1976 Vintage Computing Computers Altair 680 | |
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US $49.99 | 23d 21h 24m |
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Byte Magazine Issue #5 January 1976 Vintage Computing Computers Intel 8080 Code | |
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US $49.99 | 23d 21h 21m |
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Byte Magazine Issue #13 September 1976 Vintage Computing Computers Z80 Circuit | |
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US $24.99 | 23d 21h 35m |
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Byte Magazine Issue #10 June 1976 Vintage Computing Computers | |
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US $39.99 | 23d 21h 27m |
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Byte Magazine Issue #15 November 1976 Vintage Computing Computers Graphics | |
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US $19.99 | 23d 21h 40m |
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Byte Magazine Issue #11 July 1976 Vintage Computing Computers Printed Circuits | |
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US $29.99 | 23d 21h 29m |
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Vintage lot 7 Computer Magazines 1968+ ACM Byte Computer Craft Dr Jobb's Journal | |
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US $30.00 | 20d 18h 52m |
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Byte Magazine Collection Vintage 1970s and 1980s 25 Total | |
0 Bid | US $19.95 | 4d 12h 45m |
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Byte Magazine February 1989 - Apple Mac SE/30 | |
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US $11.05 | 26d 8h 17m |
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BYTE Magazine January 1995 Vol 20 No 1 Small Office Computing, Monitors | |
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US $19.99 | 25d 23h 31m |
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BYTE Magazine September 1993 Vol 18 No 10 Video Computing, Hard drives, | |
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US $19.99 | 26d 9h 4m |
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BYTE Magazine September 1994 Vol 19 No 9 Plug & Play, Hassle-Free PCs | |
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US $14.99 | 26d 9h 3m |
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BYTE Magazine August 1993 Vol 18 No 9 PowerPC Ethternet Cards Tomorrow's Network | |
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US $19.99 | 26d 9h 4m |
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BYTE Magazine December 1994 Vol 19 No 12 Apple's Gamble, 90MHz Pentiums | |
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US $19.99 | 26d 9h 3m |
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BYTE Magazine October 1994 Vol 19 No 10 Intelligent Networks, Portable Networks | |
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US $14.99 | 26d 9h 3m |
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BYTE Magazine August 1994 Vol 19 No 6 Document management, High-End Pentiums | |
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US $19.99 | 26d 9h 3m |
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BYTE Magazine Aoril 1997 Vol 22 No 4 network Computers, Java 1.1 | |
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US $19.99 | 26d 1h 14m |
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BYTE Magazine April 1996 Vol 21 No 4 Future Computers, Windows 95 Middleware | |
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US $19.99 | 26d 1h 14m |
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BYTE Magazine January 1994 Vol 19 No 1 Special report Advanged Operating System | |
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US $19.99 | 26d 1h 36m |
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1982 Jan BYTE Magazine THE SMALL SYSTEMS cover pic the IBM Personal Computer | |
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US $9.58 | 21d 12h 38m |
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The Best of Real Sex - HBO Presents |
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As with sex itself, the better parts of HBO's long-running magazine show Real Sex are a matter of personal preference. However, the cable channel's official greatest-hits package, The Best of Real Sex, is an accurate overview of the series' polymorphic interests: loving intimacy, outright orgies, voyeurism, transvestites, computer sex, self-pleasure. The more sensational stuff, such as a story on a Miss Nude World competition or a software company's development of cyberslut "Virtual Valerie," wears thin after a few minutes. But when the accent is on ordinary people with ordinary bodies, The Best of Real Sex is far more compelling. The first feature, for example, concerns a course in helping women of any age experience their first full orgasm through masturbation. Elsewhere, a Maui retreat presents a New Agey curriculum to individuals and partners for deepening their sexual experiences (though much of the footage looks like a carnal free-for-all). Perhaps the most interesting piece focuses on a good-looking guy who spends his lunch hour guiding a peep-show girl through a realization of his fantasies. With a window separating them, these two intelligent folks (we learn through a separate interview that she has various degrees of higher learning) slide into a loose, easy banter that makes one wonder why some men have to go to such extremes to be happy. --Tom Keogh |
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Dragon Magazine 330 April 2005 Dungeons and Dragons 3rd Edition |
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Chaos League Sudden Death (PC CD) also includes the smash hit original Chaos League game! Sale Price: $29.99 |
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Manufacturer's Description The chaos is back! Chaos League: Sudden Death takes off where the original game left off, improved and enhanced to take the real-time, role-playing strategy sports title into a whole new world of pain. The first rule of Chaos League is simple: there are no rules. The aim of the game stays the same score as many goals as possible&any way possible! Added to the brutal mix are new races, new grounds, 19 additional hero characters, improved commentary, additional in game options such as auto spell casting the ability to beat up the referee! In multiplayer, opponents can goad each other via microphone, and fresh tournament modes can be managed to create almost limitless leagues. |
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Mary Engelbreit: The Art And The Artist Hardback List Price: $29.95 Sale Price: $9.00 |
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It's been five years and several prints runs since Mary Engelbreit: The Art and The Artist was first published. Great changes and achievements mark the last five years of Mary's remarkable growth, but what's even more remarkable is what hasn't changed. Mary Engelbreit herself continues to create, by her hand alone, illstrations that have an uncanny knack for reaching an intimate place in the hearts of an ever-growing audience. Cherries, checks, and cottage roses. Straw hats, eyeglasses, vibrant colors, quotes, and decorative borders. All are familiar elements to Mary Engelbreit's millions of fans. Her style is probably more recognized than that of any contemporary illustrator. Fans and collectors around the world know her through her magazine, cards, books, calendars, and other social expression products. This authoritative book follows this amazing artist's career from the moment she first set up shop (a "studio" in her mother's linen closet), through her early years as a developing talents, and on to her current status as the world's premier illustrator. People magazine has called her "a contemporary Norman Rockwell." The Wall Street Journal recently referred to her "vast empire of cuteness." This book will give her fans the most extensive, definitive collection of Mary's works to date. |
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The BYTE Book of Pascal |
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1896 Climbing Mont Blanc in a Blizzard Col De Blanc Grands Mulets |
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Vintage Magazine Article carefully extracted from a magazine published in 1896. Article is in good condition. Approximate page size is: 6" x 9". Your satisfaction is 100 % GUARANTEED. You are purchasing a vintage magazine article, carefully extracted from a bound volume, not an entire magazine. Article contains 14 pages, 10 illustrations SKU # 1946 |
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Brothers in Arms: Hell's Highway List Price: $29.99 Sale Price: $1.99 |
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Brothers In Arms Hell's Highway brings the critically acclaimed squad-based WWII shooter into the next generation of gaming with amazing graphics and sound, new cutting-edge gameplay features, and a totally redesigned online component. Delivering on the franchise's compelling story, unrivaled authenticity and intense squad-based action, Brothers In Arms Hell's Highway drops you into Operation Market Garden, the largest paratrooper operation in World War II. Lead Matt Baker, Joe Hartsock and the rest of the 101st Airborne Division as they fight to open "Hell's Highway" in a daring bid for a quick end to the war. The Next-Gen of WWII action Take command as Sgt. Matt Baker.View larger. Engage in large scale battles.View larger. Or house-to-house combat.View larger. Experience the life of a soldier.View larger. Utilze fully-destructable cover. View larger. Precision shots yield 'quick kill' points. View larger. Operation Market Garden: The Story of Hell's HighwayOperation Market Garden was a real-life Allied offensive designed to destroy Nazi Germany before Christmas, 1944. The plan was ambitious - it was largest airborne invasion in the history of the world. The plan was to capture a corridor through Holland to punch through the German lines. Paratroopers of the 101st Airborne and other divisions dropped from the sky in mid-September to seize and hold the corridor by surprise. The surprise attack was a bust. Hitler's best units were in the area and immediately counter-attacked and crushed the corridor. Meet the Squad Although players see the chaos of Operation Market Garden exclusively through the eyes of Sgt. Matt Baker, Hell's Highway is squad-based and as such players will direct, interact with, and get to know all the members of the 101st Airborne in intimate detail. Some of the characters return from earlier games in the series, while others are brand new. Regardless, each has his own personality, history and attitudes towards the plight the squad finds themselves in as they seek to survive a bold, but doomed plan. Some of the dog faces players will encounter are include: Name & Rank: Sgt. Matt Baker Preferred Weapon: M1 RifleBio: With his experience from the battles of Normandy, Baker enters Holland fully accepting the mantle and responsibilities of squad leader. However, the loss of half his original squad still weighs heavily on him and he will not let any more of his men die. Name & Rank: Sgt. Joe "Red" Hartsock Preferred Weapon: M1A1 Thompson Bio: A corporal under Baker's command, he was promoted to Sergeant after the battle in Carentan. Unlike Baker, Red understands the realities of war and is prepared to make the tough decisions required of a squad leader. All he can do is minimize the casualties. Name & Rank: Pfc. Mike Dawson Preferred Weapon: M1A1 Thompson Bio: Though a newcomer to the squad, Mike served with the 502nd PIR in Normandy. He believes in fate and is interested in the story of Baker's "cursed pistol." His inquiries have made him a pariah, as the other members would rather not re-live those events. Name & Rank: Cpl. Tom Zanovich Preferred Weapon: M1918A2 B.A.R. Bio: The "old man" and veteran of the squad, Zanozich served in the French Foreign Legion before enlisting in the U.S. Army. Despite all the combat he's seen, Tom has a strong sense of humor - even when the situation doesn't exactly call for it. Name & Rank: Cpl. Sam Corrion Preferred Weapon: M1A1 Thompson Bio: Corrion was a corporal with Baker and Hartsock before Normandy, and much to his dismay, is still a corporal. Sam excelled as a foreman in the saw mill back home, and believes that he could perform better than either of his compatriots, if only given a chance. Key Features: Brothers In Arms Hell's Highway - Next Generation: The classic authentic, squad-based combat series explodes on your PC, offering unprecedented graphics and features. New Story, New Setting: Follow Matt Baker, Joe Hartsock and the rest of the 101st Airborne Division in "Operation Market-Garden" as they fight to open famous "Hell's Highway" in a daring bid for a quick end to the war. Live the life of an Enlisted Man: Get orders from HQ, go on patrol, spot the enemy and set up a devastating ambush. For the first time, finding the enemy before they find you is part of the challenge. Unprecedented Character Design: Lifelike characters look, talk, move and think with incredible realism. Game characters interact with the player and each other like true brothers in arms, trading ammunition, helping wounded allies and civilians, working together to man team-operated weapons, and more. Rich Cinematic Experience: As the squad leader, you interact with and get to know your brothers. Each character has his own personality, unique story and background, and grows through the game. Powerful New Units Under Your Command: Players can use or command combined arms teams - machine gun crews create intense fire, bazooka crews destroy buildings and tanks, and mortar crews pound the enemy from a distance. Step Into the Boots of a Soldier: Hit the dirt and get prone, rip grenades from your chest and hurl them at your enemies. See and feel the blast of nearby explosions. Completely New Multiplayer Experience: Fight major multiplayer battles with dozens of players on each side; all the intensity and accessibility of Deathmatch meshed with the squad-based gameplay that helped make Brothers In Arms famous. Destroyable Cover: Keep your men moving and choose your cover wisely - simulated with real physics, weapons will damage, dent, scorch and destroy the world around you. There's only one way out of hell, and that's through it. To the Allied paratroopers who fought to capture and hold the bridges and roads of Holland targeted in Operation Market Garden the price was the lives of their brothers in arms. To them the corridor became known as Hell's Highway. It was the the last great Nazi victory. It was simply hell for Sgt. Matt Baker and his squad. Relive their courage and heroism in Brothers in Arms: Hell's Highway. Brothers in Arms: Hell's Highway: The Colonel Antal Interview Since 1985, when it dropped into the WWII division of the popular tactical/strategy shooter genre with Brothers in Arms: Road to Hill 30, the Brothers in Arms series has consistently met with critical acclaim and gamer praise. This was a tough mission to accomplish, but it did so by focusing compelling squad-based tactical gameplay against the backstory of a fictional squad within the US Army's 101st Airborne Division as they battle through a historically correct re-creation of the events of the Allied invasion of Europe during WWII. This type of realism is more or less unheard of in the video game world and can only be provided by someone who really knows their stuff. Enter Colonel John Antal, US Army (Ret.). Vice President of Knowledge Operations and Military and Historical Director at Gearbox Software, developers of the Brothers in Arms series, Colonel Antal is an Airborne-Ranger officer, and a published author of scores of magazine articles, as well as several fiction and nonfiction books on military topics. Most importantly for the subject at hand though, he is also a major force behind the development of the Brothers in Arms series. Recently I was able to catch up with him and ask some questions regarding the newest title in that line, Brothers in Arms Hell's Highway. Colonel John Antal, US Army (Ret.) in Kandahar, Afghanistan in September, 2008 during a visit with the paratroopers of the 101st Airborne (Air Assault) Division Amazon.com/games: The modern military is full of cutting edge technology, but I'm sure that some readers will be surprised to hear that a career military man, such as yourself, plays such a pivotal role in the development and marketing of a video game. How did this come about? And did you have any video game experience prior to the Brothers in Arms franchise? Colonel Antal: I just returned from a ten day visit with US and NATO troops in Afghanistan, and I can report to you that when Soldiers are not fighting, they are training for the next fight. The more time Soldiers spend training in realistic simulated battles, the less blood spilled in battle. During my thirty years in the US Army, I trained thousands of Soldiers using constructive, virtual and live military simulations. Once I retired, it was an easy leap for me to switch my experience with military simulations into value-added content for video games. Amazon.com/games: I know you've probably been asked this question a thousand times, but let's make it a thousand and one. Operation Market Garden, the WWII allied offensive that Hell's Highway is based on culminated in a German victory. Why make a video game based on a defeat? Colonel Antal: Market Garden was not a complete success, but war is fought at many levels. At the squad level, war is about fighting for the next hill, hamlet or road junction. Victory and defeat is very clear in a squad - when the battle is over you either have won and lived, or have lost and most of your comrades are dead or wounded. In Brothers in Arms, Hell's Highway, we created a historical fiction that is true to the real history of the battle in ways no other game has approached. The true story of the battle for Hells Highway is dramatic and a worthy story. If we told only stories of victories, we wouldn't be telling true historical fiction. War is about victory AND defeat, gain and loss. Telling both is part of being true to the actual history. Amazon.com/games: As a follow-up to this, because this game is based on a very real and in many senses, tragic event for the forces involved and the civilian population, was there more pressure to stick to the facts in this game in order to get things right? And if so how did your team ensure this? Colonel Antal: Yes. We have done our best to make the game the most authentic WWII experience ever. Our goal is to put you in the boots of a true-to-life squad leader in the 101st Airborne during the fight for Hell's Highway. This game addresses the true history of the Battle for Hell's Highway, uses realistic tactics and requires you to lead a rifle squad in ways that no other game has ever done. Amazon.com/games: The game's story is centered around the events of an American squad, but since Operation Market Garden was an allied offensive, will players get to control, or even better, play as other allied forces? And will there be any playable German characters? Colonel Antal: The player will play as an American, Staff Sergeant Matt Baker. If you want to learn more about the British, the Dutch Resistance and the Germans, read my companion novel to the game, "Brothers in Arms, Hell's Highway", published by Ballantine Books, Random House. There will also be a history book out in January 2009 that explains the battle for Hell's Highway in day-to-day detail with maps, historical photos, screenshots from the game and much more. Shop for the Colonel's companion novel, "Brothers in Arms, Hell's Highway." Amazon.com/games: I've read that Hell's Highway will contain a whole new multiplayer experience. Can you shed some light on how this experience is different from what previous games in the series offered? Colonel Antal: Multiplayer is really exciting. Our multiplayer design is different from other games because it brings squad game play on-line. We created a team based game, where the team-based aspect is about squad organization. The game will select (or players will vote on) a commander on each team. There can be up to 20 people in the game, 10 per team. The teams will go after objectives in each map that will be resolved in rounds. Amazon.com/games: A common criticism in squad-based games is that AI enemies engaged in singleplayer modes are too easily outwitted, which is obviously something that should not happen on a real battlefield. Was dealing with this a consideration in the development of Hell's Highway? Colonel Antal: Yes, the team did a good job at creating a robust enemy AI that is clearly a tough opponent. Players will be excited about this feature. Amazon.com/games: Hell's Highway will feature a wide variety of weapons, not only used by US forces, but also those of allied troops and the Germans. Are all of these usable by any character and if so does each have specific ammo? Colonel Antal: All the small arms -- pistols and rifles -- will be useable, as well as most of the crew served weapons (like machine guns). Players can choose from semi-automatic rifles such as the M1-Carbine and M1-Garand, to machine guns such as the M1A1, bazookas such as the M9A1 and pistols. To check out all the weapons you can use and see them in action, check out the website (www.brothersinarmsgame.com in 'the game' section). Amazon.com/games: Hell's Highway is the first Brother in Arms title to come to Next-Gen platforms. How much has the visual clarity made possible on these platforms helped in the series' ongoing quest for authenticity? Colonel Antal: The visual clarity is stunning in this game. It makes use of the Unreal 3 Engine technology to deliver incredibly realistic, authentic and cutting-edge 3D environments. The game also has unprecedented character design - with lifelike characters who look, talk, move and think with incredible realism. Amazon.com/games: Hell's Highway is the third game in the series featuring Sgt. Matt Baker, and now Sgt. Joe Hartsock. These two have differing views on the acceptable costs of war and have traded off command duties in the last two games. In Hell's Highway both lead separate squads, but Sgt. Baker appears to be front and center. Will the game feature the ability to play as either sergeant? And regardless, is this dichotomy of outlook regarding command as much an attempt at realism in the field as anything else in the game? Colonel Antal: The player will play the role of Baker. The story of Baker and Hartsock, and their different approaches to leadership is a vital part of the story that I many of our fans will find intriguing. Amazon.com/games: I don't recall tanks playing too much of a role in earlier Brothers in Arms games, but one of the nicer videos I've seen indicated that they might be prominent in Hell's Highway. This seems a nice touch considering that Operation Market Garden's objective was to secure a path for armored units crossing into Germany. What exactly will players be able to do with tanks and what other new units and/or changes to the control scheme can players expect to see? Colonel Antal: The player will be able to fight as part of a Sherman Firefly crew and will be able to battle German Panzers head to head. Amazon.com/games: When last I saw Hell's Highway demoed, one of the things that stood out in my mind was the destructibility of environments and cover. Considering that the game in filled with weapons of varying destructive power, I thought this was a fantastic and natural addition. Can you give a few details on this for our readers? Colonel Antal: Destructible cover allows the player greater tactical flexibility. Now you can create a flank by blowing away flimsy cover that would normally block your maneuver. The destructible cover system was modeled using real physical properties and behaviors, so weapons will actually damage, dent, scorch and destroy the world around you. It's a great addition to the game that gives players a truly authentic experience. Amazon.com/games: Having grown up myself watching movies like A Bridge Too Far, The Guns of Navarone/Force 10 From Navarone, Where Eagles Dare, The Dirty Dozen, Kelly's Heroes, The Longest Day, etc. I've often wondered why the events of WWII have not been more heavily mined for game subject matter. With the continued success of the Brothers in Arms franchise and others, as well as the power of Next-Gen technology, do you see this changing? Colonel Antal: I strongly believe that powerful, dramatic human stories provide an exciting medium for video games. WWII was a vast WORLD WAR with countless powerful, dramatic, human stories. Anyone who is bored with WWII hasn't studied the war in depth. The stories from WWII have importance relevance to us today and are archetypical examples of the moral dilemma of war. No sane person wants war, but if you value life and liberty, sometimes you are forced to fight. I expect that we will be telling these stories and incorporating them into our games for many years to come. Amazon.com/games: Since the framework of the Brothers in Arms series is the allied invasion of German-occupied Europe and the battles that would eventually make up the march towards Berlin/the race to beat the Red Army there, can players expect to see more from Sgt. Baker and his squad in the future, perhaps at the Battle of the Bulge and beyond? Colonel Antal: As long as I have anything to do with it, the answer is YES. Amazon.com/games: Finally, because it's interesting to pick the brains of people behind the games, do you mind telling our readers what games you are playing and/or what you are reading these days? Also, I know that you have authored several fiction and nonfiction books as well as articles. Are you working on anything new that you might want to tell us about? Colonel Antal: I'm an Airborne-Ranger and have been a combat officer all my adult life. I pretty much like to play anything with WAR in the title! Many thanks to Colonel Antal for taking taking some time to give us his insights into the impact that the historical events of WWII, as well as actual combat tactics, have played in the creation of Hell's Highway and the Brothers in Arms franchise as a whole. We wish him, Gearbox Software and Ubisoft much success with the new game and look forward to the next installment in the Brothers in Arms saga, where players may find the Screaming Eagles of the 101st Airborne fighting their way to Berlin and an eventual rendezvous with VE day. --Tom Milnes, Freelance Contributor |
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Game Tycoon List Price: $19.99 Sale Price: $6.58 |
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Wele to the Game Industry!Product InformationThe year is 1982. Three young entrepreneurs discover the puter andpotential to strike it rich in the Games Industry! Their love for putergames is a mon bond that they all share but they are also in fiercepetition with one another in a race to build a successful Games DevelopmentHouse. They quickly find that the Games Development business is not assimple as it appears.Banks Investors Magazines Retailers Distributors Manufacturers and thePress are all breathing down your neck...they want results! You are now anup and ing entrepreneur in the Games Business. Do you have what ittakes to be the next Game Tycoon?Product Features Cartoon style graphics Three characters to choose from eleven mission including tutorial as well as a continuous play Interactive advisor (when you need advice) Different market situations in accordance with the customer's requests of the respective years As the years progress new technological advances offer new game development techniques Animation of all speaking characters Windows Requirements Windows 98 Me 2000 XP Pentium III 600MHz processor 128MB of RAM Sound Card DirectX 9.0c CD-ROM Drive |
Using your PC for a prolonged period of time you backup and duplicate a lot of files including music, documents, videos and images. However finding duplicate files is a tedious task as they usually are stored on different places and using different file names. A free tool was released recently that solves this issue and can restore gigabytes of valuable space in a very short time.
Most people backup different versions of documents, images edited in different formats and download the same files multiple times storing them in different locations. Often those files are forgotten and left useless in some folder on your hard drive. Nowadays the capacity of the hard drives is huge and most of us do not care too much for keeping an organized hard drive. Unfortunately as time passes this becomes a serious issue due to the fact that along with the increased hard drive space the average media file sizes have grown too.
Finding duplicate files manually is a time consuming process that usually ends up with a moderate benefits and costs a lot of time and effort. Luckily there are tools that automate the task and perform a true byte-by-byte duplicate search. That allows finding duplicate files even if they are stored using different file names.
Recently PC Magazine, ZDNet and other reputable media sources have featured a free tool that can solve this issue. Though Fast Duplicate File Finder is a relatively new product, it provides an amazing scan speed and plenty of features. The option to preview music, video, image, text and binary files before you move them to the Recycle bin, another folder or delete them permanently is really useful. You will find also free tutorials at the publisher's web site. A free download link is provided below.
Free Tool Download: Duplicate File Finder.
AN OVERVIEW OF KNOWLEDGE DISCOVERY IN DATABASE (KDD) PROCESS TOWARDS DATA MINING
1. INTRODUCTION
Historically, the notion of finding useful patterns in data has been given a variety of names, including data mining, knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing.
The rapid emergence of electronic data management methods has lead some to call recent times as the "Information Age." Powerful database systems for collecting and managing are in use in virtually all large and mid-range companies -- there is hardly a transaction that does not generate a computer record somewhere. Each year more operations are being computerized, all accumulate data on operations, activities and performance. All these data hold valuable information, e.g., trends and patterns, which could be used to improve business decisions and optimize success.
However, today’s databases contain so much data that it becomes almost impossible to manually analyze them for valuable decision-making information. In many cases, hundreds of independent attributes need to be simultaneously considered in order to accurately model system behavior. The term data mining has mostly been used by statisticians, data analysts, and the management information systems (MIS) communities. It has also gained popularity in the database field. The phrase knowledge discovery in databases was coined at the first KDD workshop in 1989 [1] (Piatetsky-Shapiro 1991) to emphasize that knowledge is the end product of a data-driven discovery. It has been popularized in the AI and machine-learning fields. In our view, KDD refers to the overall process of discovering useful knowledge from data, and data mining refers to a particular step in this process. Data mining is the application of specific algorithms for extracting patterns from data. The distinction between the KDD process and the data-mining step (within the process) is a central point of this article. The additional steps in the KDD process, such as data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining, are essential to ensure that useful knowledge is derived from the data. Blind application of data-mining methods (rightly criticized as data dredging in the statistical literature) can be a dangerous activity, easily leading to the discovery of meaningless and invalid patterns.
2. THE INTERDISCIPLINARY NATURE OF KDD
KDD has evolved, and continues to evolve, from the intersection of research fields such as machine learning, pattern recognition, databases, statistics, AI, knowledge acquisition for expert systems, data visualization, and high-performance computing. The unifying goal is extracting high-level knowledge from low-level data in the context of large data sets. The data-mining component of KDD currently relies heavily on known techniques from machine learning, pattern recognition, and statistics to find patterns from data in the data-mining step of the KDD process.
A natural question is how is KDD different from pattern recognition or machine learning (and related fields)? The answer is that these fields provide some of the data-mining methods that are used in the data-mining step of the KDD process. KDD focuses on the overall process of knowledge discovery from data, including how the data are stored and accessed, how algorithms can be scaled to massive data sets still run efficiently, how results can be interpreted and visualized, and how the overall man-machine interaction can usefully be modeled and supported.
The KDD process can be viewed as a multidisciplinary activity that encompasses techniques beyond the scope of any one particular discipline such as machine learning. In this context, there are clear opportunities for other fields of AI (besides machine learning) to contribute to KDD. KDD places a special emphasis on finding understandable patterns that can be interpreted as useful or interesting knowledge.
Thus, for example, neural networks, although a powerful modeling tool, are relatively difficult to understand compared to decision trees. KDD also emphasizes scaling and robustness properties of modeling algorithms for large noisy data sets. Related AI research fields include machine discovery, which targets the discovery of empirical laws from observation and experimentation [10] (Shrager and Langley 1990) and causal modeling for the inference of causal models from data [11] (Spirtes, Glymour, and Scheines 1993). Statistics in particular has much in common with KDD. Knowledge discovery from data is fundamentally a statistical endeavor. Statistics provides a language and framework for quantifying the uncertainty that results when one tries to infer general patterns from a particular sample of an overall population. As mentioned earlier, the term data mining has had negative connotations in statistics since the 1960s when computer-based data analysis techniques were first introduced.
The concern arose because if one searches long enough in any data set (even randomly generated data), one can find patterns that appear to be statistically significant but, in fact, are not. Clearly, this issue is of fundamental importance to KDD. Substantial progress has been made in recent years in understanding such issues in statistics. Much of this work is of direct relevance to KDD. Thus, data mining is a legitimate activity as long as one understands how to do it correctly; data mining carried out poorly (without regard to the statistical aspects of the problem) is to be avoided. KDD can also be viewed as encompassing a broader view of modeling than statistics. KDD aims to provide tools to automate (to the degree possible) the entire process of data analysis and the statistician’s “art” of hypothesis selection.
A driving force behind KDD is the database field (the second D in KDD). Indeed, the problem of effective data manipulation when data cannot fit in the main memory is of fundamental importance to KDD. Database techniques for gaining efficient data access, grouping and ordering operations when accessing data, and optimizing queries constitute the basics for scaling algorithms to larger data sets. Most data-mining algorithms from statistics, pattern recognition, and machine learning assume data are in the main memory and pay no attention to how the algorithm breaks down if only limited views of the data are possible. A related field evolving from databases is data warehousing, which refers to the popular business trend of collecting and cleaning transactional data to make them available for online analysis and decision support. Data warehousing helps set the stage for KDD in two important ways:
(1) Data Cleaning
(2) Data Access.
Data cleaning
As organizations are forced to think about a unified logical view of the wide variety of data and databases they possess, they have to address the issues of mapping data to a single naming convention, uniformly representing and handling missing data, and handling noise and errors when possible.
Data access
Uniform and well-defined methods must be created for accessing the data and providing access paths to data that were historically difficult to get to (for example, stored offline). Once organizations and individuals have solved the problem of how to store and access their data, the natural next step is the question, what else do we do with all the data? This is where opportunities for KDD naturally arise.
A popular approach for analysis of data warehouses is called online analytical processing (OLAP), named for a set of principles proposed by [12] Codd (1993). OLAP tools focus on providing multidimensional data analysis, which is superior to SQL in computing summaries and breakdowns along many dimensions. OLAP tools are targeted toward simplifying and supporting interactive data analysis, but the goal of KDD tools is to automate as much of the process as possible. Thus, KDD is a step beyond what is currently supported by most standard database systems.
3. DATA MINING AND KNOWLEDGE DISCOVERY IN THE REAL WORLD
A large degree of the current interest in KDD is the result of the media interest surrounding successful KDD applications, for example, the focus articles within the last two years in Business Week, Newsweek, Byte, PC Week, and other large-circulation periodicals. Unfortunately, it is not always easy to separate fact from media hype. Nonetheless, several well documented examples of successful systems can rightly be referred to as KDD applications and have been deployed in operational use on large-scale real-world problems in science and in business.
In science, one of the primary application areas is astronomy. Here, a notable success was achieved by SKICAT, a system used by astronomers to perform image analysis, classification, and cataloging of sky objects from sky-survey images [2] (Fayyad, Djorgovski, and Weir 1996). In its first application, the system was used to process the 3 terabytes (1012 bytes) of image data resulting from the Second Palomar Observatory Sky Survey, where it is estimated that on the order of 109 sky objects are detectable. SKICAT can outperform humans and traditional computational techniques in classifying faint sky objects. See [3] Fayyad, Haussler, and Stolorz (1996) for a survey of scientific applications.
In business, main KDD application areas includes marketing, finance (especially investment), fraud detection, manufacturing, telecommunications, and Internet agents.
Marketing
In marketing, the primary application is database marketing systems, which analyze customer databases to identify different customer groups and forecast their behavior. Business Week [4] (Berry 1994) estimated that over half of all retailers are using or planning to use database marketing, and those who do use it have good results; for example, American Express reports a 10- to 15- percent increase in credit-card use. Another notable marketing application is market-basket analysis [5] (Agrawal et al. 1996) systems, which find patterns such as, “If customer bought X, he/she is also likely to buy Y and Z.” Such patterns are valuable to retailers.
Investment
Numerous companies use data mining for investment, but most do not describe their systems. One exception is LBS Capital Management. Its system uses expert systems, neural nets, and genetic algorithms to manage portfolios totaling $600 million; since its start in 1993, the system has outperformed the broad stock market [6] (Hall, Mani, and Barr 1996).
Fraud detection
HNC Falcon and Nestor PRISM systems are used for monitoring credit card fraud, watching over millions of accounts. The FAIS system [7] (Senator et al. 1995), from the U.S. Treasury Financial Crimes Enforcement Network, is used to identify financial transactions that might indicate money laundering activity.
Manufacturing
The ASSIOPEE troubleshooting system, developed as part of a joint venture between General Electric and SNECMA, was applied by three major European airlines to diagnose and predict problems for the Boeing 737. To derive families of faults, clustering methods are used. CASSIOPEE received the European first prize for innovative applications.
Telecommunications
The telecommunications alarm-sequence analyzer (TASA) was built in cooperation with a manufacturer of telecommunications equipment and three telephone networks [8] (Mannila, Toivonen, and Verkamo 1995). The system uses a novel framework for locating frequently occurring alarm episodes from the alarm stream and presenting them as rules. Large sets of discovered rules can be explored with flexible information-retrieval tools supporting interactivity and iteration. In this way, TASA offers pruning, grouping, and ordering tools to refine the results of a basic brute-force search for rules.
Data cleaning
The MERGE-PURGE system was applied to the identification of duplicate welfare claims [9] (Hernandez and Stolfo 1995). It was used successfully on data from the Welfare Department of the State of Washington. In other areas, a well-publicized system is IBM’s ADVANCED SCOUT, a specialized data-mining system that helps National Basketball Association (NBA) coaches organize and interpret data from NBA games (U.S. News 1995). ADVANCED SCOUT was used by several of the NBA teams in 1996, including the Seattle Supersonics, which reached the NBA finals. Finally, a novel and increasingly important type of discovery is one based on the use of intelligent agents to navigate through an information-rich environment. Although the idea of active triggers has long been analyzed in the database field, really successful applications of this idea appeared only with the advent of the Internet. These systems ask the user to specify a profile of interest and search for related information among a wide variety of public-domain and proprietary sources. For example, FIREFLY is a personal music-recommendation agent: It asks a user his/her opinion of several music pieces and then suggests other music that the user might like.
4. KNOWLEDGE DISCOVERY AND DATA MINING
This section provides an introduction into the area of knowledge discovery and data mining tasks.
The Knowledge Discovery Process
There is still some confusion about the terms Knowledge Discovery in Databases (KDD) and data mining. Often these two terms are used interchangeably. We use the term KDD to denote the overall process of turning low-level data into high-level knowledge. A simple definition of KDD is as follows: Knowledge discovery in databases is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. We also adopt the commonly used definition of data mining as the extraction of patterns or models from observed data. Although at the core of the knowledge discovery process, this step usually takes only a small part (estimated at 15% to 25 %) of the overall effort. Hence data mining is just one step in the overall KDD process.
Other steps for example involve: Developing an understanding of the application domain and the goals of the data mining process Acquiring or selecting a target data set Integrating and checking the data set Data cleaning, preprocessing, and transformation Model development and hypothesis building Choosing suitable data mining algorithms Result interpretation and visualization Result testing and verification Using and maintaining the discovered knowledge.
Data Mining Tasks
At the core of the KDD process are the data mining methods for extracting patterns from data. These methods can have different goals, dependent on the intended outcome of the overall KDD process. It should also be noted that several methods with different goals may be applied successively to achieve a desired result. For example, to determine which customers are likely to buy a new product, a business analyst might need to first use clustering to segment the customer database, and then apply regression to predict buying behavior for each cluster. Most data mining goals fall under the following categories:
Data Processing
Depending on the goals and requirements of the KDD process, analysts may select, filter, aggregate, sample, clean and/or transform data. Automating some of the most typical data processing tasks and integrating them seamlessly into the overall process may eliminate or at least greatly reduce the need for programming specialized routines and for data export/import, thus improving the analyst’s productivity.
Prediction
Given a data item and a predictive model, predict the value for a specific attribute of the data item. For example, given a predictive model of credit card transactions, predict the likelihood that a specific transaction is fraudulent.
Regression
Given a set of data items, regression is the analysis of the dependency of some attribute values upon the values of other attributes in the same item, and the automatic production of a model that can predict these attribute values for new records. For example, given a data set of credit card transactions, build a model that can predict the likelihood of fraudulence for new transactions.
Classification
Given a set of predefined categorical classes, determine to which of these classes a specific data item belongs. For example, given classes of patients that corresponds to medical treatment responses; identify the form of treatment to which a new patient is most likely to respond.
Clustering
Given a set of data items, partition this set into a set of classes such that items with similar characteristics are grouped together. Clustering is best used for finding groups of items that are similar. For example, given a data set of customers, identify subgroups of customers that have a similar buying behavior.
Link Analysis (Associations)
Given a set of data items, identify relationships between attributes and items such as the presence of one pattern implies the presence of another pattern. These relations may be associations between attributes within the same data item. The investigation of relationships between items over a period of time is also often referred to as ‘sequential pattern analysis’.
Model Visualization
Visualization plays an important role in making the discovered knowledge understandable and interpretable by humans. Besides, the human eye-brain system itself still remains the best pattern-recognition device known. Visualization techniques may range from simple scatter plots and histogram plots over parallel coordinates to 3D movies.
5. THE DATA-MINING STEP OF THE KDD PROCESS
The data-mining component of the KDD process often involves repeated iterative application of particular data-mining methods. This section presents an overview of the primary goals of data mining, a description of the methods used to address these goals, and a brief description of the data-mining algorithms that incorporate these methods. The knowledge discovery goals are defined by the intended use of the system.
We can distinguish two types of goals:
(1) Verification
(2) Discovery.
With verification, the system is limited to verifying the user’s hypothesis. With discovery, the system autonomously finds new patterns. We further subdivide the discovery goal into prediction, where the system finds patterns for predicting the future behavior of some entities, and description, where the system finds patterns for presentation to a user in a human-understandable form.
In this article, we are primarily concerned with discovery-oriented data mining. Data mining involves fitting models to, or determining patterns from, observed data. The fitted models play the role of inferred knowledge: Whether the models reflect useful or interesting knowledge is part of the over all, interactive KDD process where subjective human judgment is typically required.
Two primary mathematical formalisms are used in model fitting:
(1) Statistical
(2) Logical.
The statistical approach allows for nondeterministic effects in the model, whereas a logical model is purely deterministic. We focus primarily on the statistical approach to data mining, which tends to be the most widely used basis for practical data-mining applications given the typical presence of uncertainty in real-world data-generating processes.
Most data-mining methods are based on tried and tested techniques from machine learning, pattern recognition, and statistics: classification, clustering, regression, and so on. The array of different algorithms under each of these headings can often be bewildering to both the novice and the experienced data analyst. It should be emphasized that of the many data-mining methods advertised in the literature, there are really only a few fundamental techniques.
6. RESEARCH AND APPLICATION CHALLENGES
We outline some of the current primary research and application challenges for KDD.
This list is by no means exhaustive and is intended to give the reader a feel for the types of problem that KDD practitioners wrestle with.
Larger databases
Databases with hundreds of fields and tables and millions of records and of a multi gigabyte size are commonplace, and terabyte (1012 bytes) databases are beginning to appear. Methods for dealing with large data volumes include more efficient algorithms sampling, approximation, and massively parallel processing.
High dimensionality
Not only is there often a large number of records in the database, but there can also be a large number of fields (attributes, variables); so, the dimensionality of the problem is high. A high-dimensional data set creates problems in terms of increasing the size of the search space for model induction in a combinatorial explosive manner. In addition, it increases the chances that a data-mining algorithm will find spurious patterns that are not valid in general. Approaches to this problem include methods to reduce the effective dimensionality of the problem and the use of prior knowledge to identify irrelevant variables.
Over fitting
When the algorithm searches for the best parameters for one particular model using a limited set of data, it can model not only the general patterns in the data but also any noise specific to the data set, resulting in poor performance of the model on test data. Possible solutions include cross-validation, regularization, and other sophisticated statistical strategies.
Assessing of statistical significance
A problem (related to over fitting) occurs when the system is searching over many possible models. For example, if a system tests models at the 0.001 significance level, then on average, with purely random data, N/1000 of these models will be accepted as significant edge is important in all the steps of the KDD process. Bayesian approaches [13] (for example, Cheeseman [1990]) use prior probabilities over data and distributions as one form of encoding prior knowledge. Others employ deductive database capabilities to discover knowledge that is then used to guide the data-mining search [14] (for example, Simoudis, Livezey, and Kerber [1995]).
Integration with other systems
A standalone discovery system might not be very useful. Typical integration issues include integration with a database management system (for example, through a query interface), integration with spreadsheets and visualization tools, and accommodating of real-time sensor readings. Examples of integrated KDD systems are described by [14] Simoudis, Livezey, and Kerber (1995).
7. CONCLUSION
This article represents a step toward a common framework that We hope will ultimately provide a unifying vision of the common overall goals and methods used in KDD. We hope this would eventually lead to a better understanding of the variety of approaches in this multidisciplinary field and how they fit together.
9. REFERENCES
[1] Piatetsky - Shapiro, G. 1991. Knowledge Discovery in Real Databases: A Report on the IJCAI-89 Workshop. AI Magazine 11(5): 68–70.
[2] Fayyad, U. M.; Djorgovski, S. G.; and Weir, N. 1996. From Digitized Images to On-Line Catalogs: Data Mining a Sky Survey. AI Magazine 17(2): 51–66.
[3] Fayyad, U. M.; Haussler, D.; and Stolorz, Z. 1996. KDD for Science Data Analysis: Issues and Examples. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 50–56. Menlo Park, Calif.: American Association for Artificial Intelligence.
[4] Berry, J. 1994. Database Marketing. Business Week, September 5, 56–62.
[5] Agrawal, R., and Psaila, G. 1995. Active Data Mining. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), 3–8. Menlo Park, Calif.: American Association for Artificial Intelligence
[6] Hall, J.; Mani, G.; and Barr, D. 1996. Applying Computational Intelligence to the Investment Process. In Proceedings of CIFER-96: Computational Intelligence in Financial Engineering. Washington, D.C.: IEEE Computer Society.
[7] Senator, T.; Goldberg, H. G.; Wooton, J.; Cottini, M. A.; Umarkhan, A. F.; Klinger, C. D.; Llamas, W. M.; Marrone, M. P.; and Wong, R. W. H. 1995. The Financial Crimes Enforcement Network AI System (FAIS): Identifying Potential Money Laundering from Reports of Large Cash Transactions. AI Magazine 16(4): 21–39.
[8] Mannila, H.; Toivonen, H.; and Verkamo, A. I. 1995. Discovering Frequent Episodes in Sequences. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), 210–215. Menlo Park, Calif.: American Association for Artificial Intelligence.
About the Author
Mr.G.PANDIYAN,
LECTURER,
DEPT OF MCA,
RVS COLLEGE OF ARTS & SCIENCE,SULUR,COIMBATORE-641662
Wow, why do almost all major economists endorse Obama?
Source:
http://www.economist.com/world/unitedstates/displaystory.cfm?story_id=12342127
It's from this week's Economist Magazine, which usually leans to the right so don't even try to use the pathetic 'liberal media' sound byte.
It's simple. Put money into the hands of everyone, including poor people, and it means more people contribute to the economy. That's why the stimulus checks worked, if only for a short time. Make the payment larger and people put the money back into the economy. Sustain their income with decent jobs and it creates more jobs, helping everyone no matter their tax bracket.
After the Great Depression, Hoover did nothing, saying the free market would correct itself. We sank lower and lower. When FDR was elected, he created jobs to put people back to work. Yes, government involvement does work. Deregulation got us into this crisis.
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