1. An image reading apparatus, comprising:
a sheet feed tray;
a supply roller configured to rotate while being in contact with an original sheet placed on the sheet feed tray and to send out the original sheet one sheet by one sheet to a conveyance path;
a conveyance unit including a first conveyance roller and a second conveyance roller and configured to convey, along the conveyance path, the original sheet that has been sent out by the supply roller to the conveyance path, the conveyance unit conveying the original sheet by using the first conveyance roller and the second conveyance roller in succession in this order;
a discharged-sheet receiving unit, into which the original sheet that has been conveyed by the conveyance unit is discharged;
an image reading unit disposed at a reading position along the conveyance path between the first conveyance roller and the second conveyance roller and configured to read an image, in a main scanning direction, from the original sheet conveyed by the conveyance unit at the reading position;
a sensor disposed at a detection position that is defined along the conveyance path upstream of the reading position, the sensor being configured to detect the original sheet; and
a control device configured to:
judge whether the original sheet is either one of a first sheet type, whose sheet length in a conveying direction along the conveyance path is longer than or equal to a first conveyance distance and shorter than a second conveyance distance, and a second sheet type, whose sheet length in the conveying direction along the conveyance path is longer than or equal to the second conveyance distance, the first conveyance distance being a distance between the first conveyance roller and the second conveyance roller along the conveyance path, the second conveyance distance being a distance between the supply roller and the second conveyance roller along the conveyance path;
set at least one of a first parameter and a second parameter dependently on the judged result, the first parameter indicating a first length of time, the first length of time being a conveyance time between when a leading edge of the original sheet in the conveying direction reaches the detection position and when the leading edge of the original sheet in the conveying direction reaches the reading position, the second parameter indicating a second length of time, the second length of time being a conveyance time between when a trailing edge of the original sheet in the conveying direction reaches the detection position and when the trailing edge of the original sheet in the conveying direction reaches the reading position; and
control the image reading unit to start reading an image from the original sheet when the first length of time has passed after the sensor detected the leading edge of the original sheet and control the image reading unit to stop reading the image from the original sheet when the second length of time has passed after the sensor detected the trailing edge of the original sheet.
2. The image reading apparatus as claimed in claim 1, wherein the control device is configured to further set a sub-scanning magnification dependently on the judged result, the sub-scanning magnification indicating an enlargement or reduction ratio in a sub-scanning direction orthogonal to the main scanning direction that is to be applied onto the image read from the original sheet by the image reading unit; and
modify, by using the set sub-scanning magnification, image data indicative of the image that is read from the original sheet by the image reading unit.
3. The image reading apparatus as claimed in claim 2, wherein
the supply roller is disposed in part of a conveyance region of the conveyance path in the main scanning direction,
each of the first and second conveyance rollers includes a plurality of roller portions arranged in the main scanning direction,
if the original sheet is determined to be of the second sheet type, the control device is configured to further judge whether the original sheet is of a third sheet type or a fourth sheet type, the number of a roller portion that constitutes the first conveyance roller and that conveys an original sheet of the third sheet type being smaller than or equal to a reference number, the number of a roller portion that constitutes the second conveyance roller and that conveys an original sheet of the third sheet type being smaller than or equal to a reference number, the number of a roller portion that constitutes at least one of the first and second conveyance rollers and that conveys an original sheet of the fourth sheet type being greater than the reference number,
the control device is configured to set the sub-scanning magnification and at least one of the first parameter and the second parameter dependently on whether the original sheet is of the third sheet type or the fourth sheet type.
4. The image reading apparatus as claimed in claim 3, wherein
the discharged-sheet receiving unit includes a first sheet discharge tray and a second sheet discharge tray,
the conveying path diverges into a first conveyance path and a second conveyance path at a position downstream of the second conveyance roller in the conveying direction,
the conveyance unit conveys original sheets of the first and third sheet types using the first conveyance path to the first sheet discharge tray, and conveys an original sheet of the fourth sheet type using the second conveyance path to the second sheet discharge tray.
5. The image reading apparatus as claimed in claim 2, further comprising a temperature sensor configured to detect temperature of the image reading apparatus, and
wherein the control device is configured to set the sub-scanning magnification and at least one of the first parameter and the second parameter dependently on the temperature detected by the temperature sensor.
6. The image reading apparatus as claimed in claim 5, wherein as the temperature is higher, the first length of time and the second length of time are set shorter and the sub-scanning magnification is set higher.
7. The image reading apparatus as claimed in claim 1, wherein the image reading unit includes:
a first image reading unit disposed at a first reading position along the conveying path between the first and second conveyance rollers and configured to read one surface of the original sheet; and
a second image reading unit disposed at a second reading position along the conveying path between the first and second conveyance rollers and configured to read another surface of the original sheet.
8. The image reading apparatus as claimed in claim 1, further comprising an operation unit configured to allow a user to input data indicative of a sheet size of the original sheet, and
wherein the control device detects a sheet length of the original sheet based on the inputted data indicative of the sheet size, thereby determining the sheet type of the original sheet.
9. An image reading apparatus, comprising:
a sheet feed tray;
a supply roller configured to rotate while being in contact with an original sheet placed on the sheet feed tray and to send out the original sheet one sheet by one sheet to a conveyance path;
a conveyance unit including a first conveyance roller and a second conveyance roller and configured to convey, along the conveyance path, the original sheet that has been sent out by the supply roller to the conveyance path, the conveyance unit conveying the original sheet by using the first conveyance roller and the second conveyance roller in succession in this order;
a discharged-sheet receiving unit, into which the original sheet that has been conveyed by the conveyance unit is discharged;
an image reading unit disposed at a reading position along the conveyance path between the first conveyance roller and the second conveyance roller and configured to read an image, in a main scanning direction, from the original sheet conveyed by the conveyance unit at the reading position; and
a control device configured to:
judge whether the original sheet is either one of a first sheet type, whose sheet length in a conveying direction along the conveyance path is longer than or equal to a first conveyance distance and shorter than a second conveyance distance, and a second sheet type, whose sheet length in the conveying direction along the conveyance path is longer than or equal to the second conveyance distance, the first conveyance distance being a distance between the first conveyance roller and the second conveyance roller along the conveyance path, the second conveyance distance being a distance between the supply roller and the second conveyance roller along the conveyance path;
set a sub-scanning magnification dependently on the judged result, the sub-scanning magnification indicating an enlargement or reduction ratio in a sub-scanning direction orthogonal to the main scanning direction that is to be applied onto an image to be read by the image reading unit; and
control the image reading unit to read an image from the original sheet and generate image data indicative of the image read from the original sheet, and modify the image data by using the sub-scanning magnification.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method comprising:
obtaining access to raw historic information technology migration data;
transforming said raw historic information technology migration data in accordance with a classification scheme to obtain classified key historic information technology migration data;
building a statistical model with conditional distributions of said key historic information technology migration data;
prior to a new information technology migration project, obtaining access to incomplete data characterizing said new information technology migration project;
using said statistical model to derive data missing from said incomplete data characterizing said new information technology migration project, to obtain derived data; and
creating at least one of a migration plan and an estimate for said new information technology migration project based on said incomplete data and said derived data.
2. The method of claim 1, further comprising deriving said classification scheme according to influence of classes on migration plans, based on at least one existing migration plan with associated real costs.
3. The method of claim 2, wherein said deriving comprises deriving said classification scheme with at least two classes comprising at least two of operating system, software type, hardware type, storage type, criticality, and clusteredness.
4. The method of claim 3, wherein:
said deriving comprises deriving said classification scheme with attributes for each of said at least two classes;
said at least two classes comprise operating system and hardware type;
said attributes for operating system comprise class, name, and version;
said attributes for hardware comprise whether virtualized, platform type, vendor, model, age, and size;
said statistical model comprises conditional probability of whether hardware is virtualized based on operating system class;
said incomplete data comprises operating system per server type without data indicating whether virtualized; and
said derived data comprises whether virtualized per operating system per server type, in accordance with said conditional probability.
5. The method of claim 3, wherein:
said deriving comprises deriving said classification scheme with attributes for each of said at least two classes;
said at least two classes comprise operating system and hardware type;
said attributes for operating system comprise class, name, and version;
said attributes for hardware comprise whether virtualized, platform type, vendor, model, age, and size;
said statistical model comprises conditional probability of operating system class based on whether hardware is virtualized;
said incomplete data comprises data indicating whether virtualized without operating system per server type; and
said derived data comprises operating system per server type, in accordance with said conditional probability.
6. The method of claim 1, further comprising augmenting said statistical model by general constraints.
7. The method of claim 1, wherein said incomplete data comprises first incomplete data, said new information technology migration project comprises a first new information technology migration project, said derived data comprises first derived data, said statistical model comprises a first statistical model, and said at least one of a migration plan and an estimate comprises a first at least one of a migration plan and an estimate, further comprising:
prior to a second new information technology migration project, obtaining access to second incomplete data characterizing said second new information technology migration project;
determining that said second incomplete data do not fit said first statistical model;
building a second statistical model based on said second incomplete data and said classified key historic information technology migration data;
using said second statistical model to derive data missing from said second incomplete data characterizing said second new information technology migration project, to obtain second derived data; and
creating at least one of a migration plan and an estimate for said new information technology migration project based on said incomplete data and said derived data.
8. The method of claim 1, wherein, in said incomplete data, data is missing across multi-dimensions, further comprising filling missing data for some of said dimensions using patterns from said historical data, and using patterns from existing partial data to augment remaining ones of said dimensions with missing data.
9. The method of claim 1, further comprising providing a system, wherein the system comprises distinct software modules, each of the distinct software modules being embodied on a computer-readable storage medium, and wherein the distinct software modules comprise a database access module, a customer data estimation module, and a plan generation engine module;
wherein:
at least said obtaining of said access to said raw historic information technology migration data and said obtaining of said access to said incomplete data characterizing said new information technology migration project are carried out by said database access module executing on at least one hardware processor;
said using of said statistical model to derive said data missing from said incomplete data characterizing said new information technology migration project, to obtain said derived data, is carried out by said customer data estimation module executing on said at least one hardware processor; and
said creating said at least one of a migration plan and an estimate for said new information technology migration project based on said incomplete data and said derived data comprises creating at least said migration plan using said plan generation engine module executing on said at least one hardware processor.
10. An apparatus comprising:
a memory; and
at least one processor, coupled to said memory, and operative to:
obtain access to raw historic information technology migration data;
transform said raw historic information technology migration data in accordance with a classification scheme to obtain classified key historic information technology migration data;
build a statistical model with conditional distributions of said key historic information technology migration data;
prior to a new information technology migration project, obtain access to incomplete data characterizing said new information technology migration project;
use said statistical model to derive data missing from said incomplete data characterizing said new information technology migration project, to obtain derived data; and
create at least one of a migration plan and an estimate for said new information technology migration project based on said incomplete data and said derived data.
11. The apparatus of claim 10, wherein said at least one processor is further operative to derive said classification scheme according to influence of classes on migration plans, based on at least one existing migration plan with associated real costs.
12. The apparatus of claim 11, wherein said deriving comprises deriving said classification scheme with at least two classes comprising at least two of operating system, software type, hardware type, storage type, criticality, and clusteredness.
13. The apparatus of claim 12, wherein:
said deriving comprises deriving said classification scheme with attributes for each of said at least two classes;
said at least two classes comprise operating system and hardware type;
said attributes for operating system comprise class, name, and version;
said attributes for hardware comprise whether virtualized, platform type, vendor, model, age, and size;
said statistical model comprises conditional probability of whether hardware is virtualized based on operating system class;
said incomplete data comprises operating system per server type without data indicating whether virtualized; and
said derived data comprises whether virtualized per operating system per server type, in accordance with said conditional probability.
14. The apparatus of claim 12, wherein:
said deriving comprises deriving said classification scheme with attributes for each of said at least two classes;
said at least two classes comprise operating system and hardware type;
said attributes for operating system comprise class, name, and version;
said attributes for hardware comprise whether virtualized, platform type, vendor, model, age, and size;
said statistical model comprises conditional probability of operating system class based on whether hardware is virtualized;
said incomplete data comprises data indicating whether virtualized without operating system per server type; and
said derived data comprises operating system per server type, in accordance with said conditional probability.
15. The apparatus of claim 10, wherein said at least one processor is further operative to augment said statistical model by general constraints.
16. The apparatus of claim 10, wherein said incomplete data comprises first incomplete data, said new information technology migration project comprises a first new information technology migration project, said derived data comprises first derived data, said statistical model comprises a first statistical model, and said at least one of a migration plan and an estimate comprises a first at least one of a migration plan and an estimate, wherein said at least one processor is further operative to:
prior to a second new information technology migration project, obtain access to second incomplete data characterizing said second new information technology migration project;
determine that said second incomplete data do not fit said first statistical model;
build a second statistical model based on said second incomplete data and said classified key historic information technology migration data;
use said second statistical model to derive data missing from said second incomplete data characterizing said second new information technology migration project, to obtain second derived data; and
create at least one of a migration plan and an estimate for said new information technology migration project based on said incomplete data and said derived data.
17. The apparatus of claim 10, wherein, in said incomplete data, data is missing across multi-dimensions, wherein said at least one processor is further operative to fill missing data for some of said dimensions using patterns from said historical data, and use patterns from existing partial data to augment remaining ones of said dimensions with missing data.
18. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, and wherein the program instructions are executable by a processor to cause the processor to perform a method comprising:
obtaining access to raw historic information technology migration data;
transforming said raw historic information technology migration data in accordance with a classification scheme to obtain classified key historic information technology migration data;
building a statistical model with conditional distributions of said key historic information technology migration data;
prior to a new information technology migration project, obtaining access to incomplete data characterizing said new information technology migration project;
using said statistical model to derive data missing from said incomplete data characterizing said new information technology migration project, to obtain derived data; and
creating at least one of a migration plan and an estimate for said new information technology migration project based on said incomplete data and said derived data.
19. The computer program product of claim 18, wherein the program instructions are further executable by a processor to cause the processor to perform a method further comprising deriving said classification scheme according to influence of classes on migration plans, based on at least one existing migration plan with associated real costs.
20. The computer program product of claim 19, wherein said deriving comprises deriving said classification scheme with at least two classes comprising at least two of operating system, software type, hardware type, storage type, criticality, and clusteredness.