Advances in portable computing and Internet service technologies (Byun and Hong, 2000) have resulted in extensive use of portable information devices such as PDAs (Personal Digital Assistants), HPCs (Hand-held PCs), and PPCs (Pocket PCs) in e-commerce environments. Each information device may run several applications, such as a small database, a personal information management system (Greer and Murtaza, 2003), and e-commerce software (Gyeung and Lee, 2003). Flash memory is one of the best candidates to support small information devices for data management in portable computing environments (Byun, 2006).

Recently, flash memory has become a critical component in building embedded systems or portable devices because it is nonvolatile, shock-resistant, and uses little power. Its performance has improved to a level at which it can be used not only as the main storage for portable computers but also as mass storage for general computing systems (Chang and Kuo, 2005). Although flash memory is not as fast as RAM, it is hundreds of times faster than a hard disk in read operations. These attractive features make flash memory one of the best choices for portable information systems (Yim, 2005).

However, flash memory has two critical drawbacks. First, a segment, blocks of flash memory, need to be erased before they can be rewritten. This is because flash memory technology only allows individual bits to be toggled in one way for writes. The erase operation writes ones or zeros into all the bits in a segment. This erase operation takes much longer than a read or write operation. The second drawback is that the life of each memory block is limited to 1,000,000 writes. A flash management system should wear down all memory blocks as evenly as possible (Kim and Lee, 1999).

Due to these disadvantages, traditional database technologies are not easy to apply directly to flash memory databases on portable devices. A database management system which is based on flash memory media must exploit the advantages of flash memory features while overcoming its constraints.

According to the text above, it is correct to affirm that

as a result of the life of its memory blocks, flash memory is the most popular storage media for information management in portable computing systems.

Advances in portable computing and Internet service technologies (Byun and Hong, 2000) have resulted in extensive use of portable information devices such as PDAs (Personal Digital Assistants), HPCs (Hand-held PCs), and PPCs (Pocket PCs) in e-commerce environments. Each information device may run several applications, such as a small database, a personal information management system (Greer and Murtaza, 2003), and e-commerce software (Gyeung and Lee, 2003). Flash memory is one of the best candidates to support small information devices for data management in portable computing environments (Byun, 2006).

Recently, flash memory has become a critical component in building embedded systems or portable devices because it is nonvolatile, shock-resistant, and uses little power. Its performance has improved to a level at which it can be used not only as the main storage for portable computers but also as mass storage for general computing systems (Chang and Kuo, 2005). Although flash memory is not as fast as RAM, it is hundreds of times faster than a hard disk in read operations. These attractive features make flash memory one of the best choices for portable information systems (Yim, 2005).

However, flash memory has two critical drawbacks. First, a segment, blocks of flash memory, need to be erased before they can be rewritten. This is because flash memory technology only allows individual bits to be toggled in one way for writes. The erase operation writes ones or zeros into all the bits in a segment. This erase operation takes much longer than a read or write operation. The second drawback is that the life of each memory block is limited to 1,000,000 writes. A flash management system should wear down all memory blocks as evenly as possible (Kim and Lee, 1999).

Due to these disadvantages, traditional database technologies are not easy to apply directly to flash memory databases on portable devices. A database management system which is based on flash memory media must exploit the advantages of flash memory features while overcoming its constraints.

According to the text above, it is correct to affirm that

due to advances in portable computing and Internet service technologies, applying traditional database technologies to flash memory databases on portable devices poses no difficulties.

Advances in portable computing and Internet service technologies (Byun and Hong, 2000) have resulted in extensive use of portable information devices such as PDAs (Personal Digital Assistants), HPCs (Hand-held PCs), and PPCs (Pocket PCs) in e-commerce environments. Each information device may run several applications, such as a small database, a personal information management system (Greer and Murtaza, 2003), and e-commerce software (Gyeung and Lee, 2003). Flash memory is one of the best candidates to support small information devices for data management in portable computing environments (Byun, 2006).

Recently, flash memory has become a critical component in building embedded systems or portable devices because it is nonvolatile, shock-resistant, and uses little power. Its performance has improved to a level at which it can be used not only as the main storage for portable computers but also as mass storage for general computing systems (Chang and Kuo, 2005). Although flash memory is not as fast as RAM, it is hundreds of times faster than a hard disk in read operations. These attractive features make flash memory one of the best choices for portable information systems (Yim, 2005).

However, flash memory has two critical drawbacks. First, a segment, blocks of flash memory, need to be erased before they can be rewritten. This is because flash memory technology only allows individual bits to be toggled in one way for writes. The erase operation writes ones or zeros into all the bits in a segment. This erase operation takes much longer than a read or write operation. The second drawback is that the life of each memory block is limited to 1,000,000 writes. A flash management system should wear down all memory blocks as evenly as possible (Kim and Lee, 1999).

Due to these disadvantages, traditional database technologies are not easy to apply directly to flash memory databases on portable devices. A database management system which is based on flash memory media must exploit the advantages of flash memory features while overcoming its constraints.

According to the text above, it is correct to affirm that

despite being hundreds of times faster than a hard disk in read operations, flash memory is now one of the best choices for portable information systems.

Advances in portable computing and Internet service technologies (Byun and Hong, 2000) have resulted in extensive use of portable information devices such as PDAs (Personal Digital Assistants), HPCs (Hand-held PCs), and PPCs (Pocket PCs) in e-commerce environments. Each information device may run several applications, such as a small database, a personal information management system (Greer and Murtaza, 2003), and e-commerce software (Gyeung and Lee, 2003). Flash memory is one of the best candidates to support small information devices for data management in portable computing environments (Byun, 2006).

Recently, flash memory has become a critical component in building embedded systems or portable devices because it is nonvolatile, shock-resistant, and uses little power. Its performance has improved to a level at which it can be used not only as the main storage for portable computers but also as mass storage for general computing systems (Chang and Kuo, 2005). Although flash memory is not as fast as RAM, it is hundreds of times faster than a hard disk in read operations. These attractive features make flash memory one of the best choices for portable information systems (Yim, 2005).

However, flash memory has two critical drawbacks. First, a segment, blocks of flash memory, need to be erased before they can be rewritten. This is because flash memory technology only allows individual bits to be toggled in one way for writes. The erase operation writes ones or zeros into all the bits in a segment. This erase operation takes much longer than a read or write operation. The second drawback is that the life of each memory block is limited to 1,000,000 writes. A flash management system should wear down all memory blocks as evenly as possible (Kim and Lee, 1999).

Due to these disadvantages, traditional database technologies are not easy to apply directly to flash memory databases on portable devices. A database management system which is based on flash memory media must exploit the advantages of flash memory features while overcoming its constraints.

According to the text above, it is correct to affirm that

flash memory has become a major database storage in building portable information devices because of its nonvolatile, shock-resistant, power-economic nature, and fast access time for read operations.

This text refers to item.

While there is no shortage of studies into the reasons why software projects fail (Ewusi-Mensah, 1997), the major risks of software development (Jones, 1994), or even the factors affecting project success (Cooke-Davies, 2002), the field of software engineering lacks a general model with which to investigate such failures. To date, studies have tended to be surveys of the factors thought to play some part in a failure.

Several researchers have argued that a simple model of accidents is insufficient for dealing with modern technology. A causal-chain model of accidents is useful to investigate the failure of a specific component through wear and tear, or the attribution of the cause can be established through application of a "but for" test. Given the cause, similar accidents can be prevented by checking the same component for wear and tear or other flaws such as structural cracks. However, it is a less useful model when investigating accidents which causes are ultimately not due to physical weaknesses but are due to interactions between components or the failure of the system itself.

Driven by the need to find ways to prevent future accidents, the alternative models reject the simple causal chain model on several grounds. The first is that looking back along the causal chain requires a "stopping rule" to determine when to cease investigating deeper into the system which, it is argued, can be somewhat arbitrary in the choice of cause (Leveson, 2004). The second reason is that such investigative techniques tend to focus attention on the proximate event most closely associated with the accident and direct attention away from the latent, contributory causes.

Where, in the past, it may have been sufficient to seek direct causes of an accident, modern socio-technical systems can produce accidents that are the result of the interaction of different parts of the system rather than a failure of any one part of the system. Turner & Pidgeon (1997) reviewed official investigations into non-natural disasters to arrive at a view that many disasters were man-made and entirely foreseeable. In a major contrast to causal models of accidents, the authors argued that the conditions for the disasters he investigated largely originated from decisions made by upper management.

The view that there was ample evidence of impending disaster available if only someone paid it any attention appears to be shared by investigators other than Turner. However such hindsight bias has been criticised by several researchers, most notably Dekker (2005). Hindsight bias ignores the reality that most operational decisions are made under ambiguous circumstances based on sparse and ambiguous evidence. Instead, Dekker argues, investigators must try hard to understand the circumstances of the time and put aside knowledge of the outcome.

To reason more fully about the interaction of different parts of a socio-technical system, several researchers have proposed a system theoretic model in which the system is expressed as a hierarchy of control levels. Each level of the hierarchy is considered to act on the level below it through the imposition of constraints and directions to achieve emergent properties and to receive feedback. A more useful model for considering total risk was a "top-down, systems oriented approach based on system control theoretic concepts". This approach gave a control structure embedded in an adaptive socio-technical system. Such a model shows how different parties contribute to safety regardless of their organizational affiliations.

In the text,

"outcome" is closest in meaning to upshot.

In the text,

"due to physical weaknesses" is synonymous with because of.

This text refers to item.

While there is no shortage of studies into the reasons why software projects fail (Ewusi-Mensah, 1997), the major risks of software development (Jones, 1994), or even the factors affecting project success (Cooke-Davies, 2002), the field of software engineering lacks a general model with which to investigate such failures. To date, studies have tended to be surveys of the factors thought to play some part in a failure.

Several researchers have argued that a simple model of accidents is insufficient for dealing with modern technology. A causal-chain model of accidents is useful to investigate the failure of a specific component through wear and tear, or the attribution of the cause can be established through application of a "but for" test. Given the cause, similar accidents can be prevented by checking the same component for wear and tear or other flaws such as structural cracks. However, it is a less useful model when investigating accidents which causes are ultimately not due to physical weaknesses but are due to interactions between components or the failure of the system itself.

Driven by the need to find ways to prevent future accidents, the alternative models reject the simple causal chain model on several grounds. The first is that looking back along the causal chain requires a "stopping rule" to determine when to cease investigating deeper into the system which, it is argued, can be somewhat arbitrary in the choice of cause (Leveson, 2004). The second reason is that such investigative techniques tend to focus attention on the proximate event most closely associated with the accident and direct attention away from the latent, contributory causes.

Where, in the past, it may have been sufficient to seek direct causes of an accident, modern socio-technical systems can produce accidents that are the result of the interaction of different parts of the system rather than a failure of any one part of the system. Turner & Pidgeon (1997) reviewed official investigations into non-natural disasters to arrive at a view that many disasters were man-made and entirely foreseeable. In a major contrast to causal models of accidents, the authors argued that the conditions for the disasters he investigated largely originated from decisions made by upper management.

The view that there was ample evidence of impending disaster available if only someone paid it any attention appears to be shared by investigators other than Turner. However such hindsight bias has been criticised by several researchers, most notably Dekker (2005). Hindsight bias ignores the reality that most operational decisions are made under ambiguous circumstances based on sparse and ambiguous evidence. Instead, Dekker argues, investigators must try hard to understand the circumstances of the time and put aside knowledge of the outcome.

To reason more fully about the interaction of different parts of a socio-technical system, several researchers have proposed a system theoretic model in which the system is expressed as a hierarchy of control levels. Each level of the hierarchy is considered to act on the level below it through the imposition of constraints and directions to achieve emergent properties and to receive feedback. A more useful model for considering total risk was a "top-down, systems oriented approach based on system control theoretic concepts". This approach gave a control structure embedded in an adaptive socio-technical system. Such a model shows how different parties contribute to safety regardless of their organizational affiliations.

In the text,

"cracks" means breakage.

This text refers to item.

While there is no shortage of studies into the reasons why software projects fail (Ewusi-Mensah, 1997), the major risks of software development (Jones, 1994), or even the factors affecting project success (Cooke-Davies, 2002), the field of software engineering lacks a general model with which to investigate such failures. To date, studies have tended to be surveys of the factors thought to play some part in a failure.

Several researchers have argued that a simple model of accidents is insufficient for dealing with modern technology. A causal-chain model of accidents is useful to investigate the failure of a specific component through wear and tear, or the attribution of the cause can be established through application of a "but for" test. Given the cause, similar accidents can be prevented by checking the same component for wear and tear or other flaws such as structural cracks. However, it is a less useful model when investigating accidents which causes are ultimately not due to physical weaknesses but are due to interactions between components or the failure of the system itself.

Driven by the need to find ways to prevent future accidents, the alternative models reject the simple causal chain model on several grounds. The first is that looking back along the causal chain requires a "stopping rule" to determine when to cease investigating deeper into the system which, it is argued, can be somewhat arbitrary in the choice of cause (Leveson, 2004). The second reason is that such investigative techniques tend to focus attention on the proximate event most closely associated with the accident and direct attention away from the latent, contributory causes.

Where, in the past, it may have been sufficient to seek direct causes of an accident, modern socio-technical systems can produce accidents that are the result of the interaction of different parts of the system rather than a failure of any one part of the system. Turner & Pidgeon (1997) reviewed official investigations into non-natural disasters to arrive at a view that many disasters were man-made and entirely foreseeable. In a major contrast to causal models of accidents, the authors argued that the conditions for the disasters he investigated largely originated from decisions made by upper management.

The view that there was ample evidence of impending disaster available if only someone paid it any attention appears to be shared by investigators other than Turner. However such hindsight bias has been criticised by several researchers, most notably Dekker (2005). Hindsight bias ignores the reality that most operational decisions are made under ambiguous circumstances based on sparse and ambiguous evidence. Instead, Dekker argues, investigators must try hard to understand the circumstances of the time and put aside knowledge of the outcome.

To reason more fully about the interaction of different parts of a socio-technical system, several researchers have proposed a system theoretic model in which the system is expressed as a hierarchy of control levels. Each level of the hierarchy is considered to act on the level below it through the imposition of constraints and directions to achieve emergent properties and to receive feedback. A more useful model for considering total risk was a "top-down, systems oriented approach based on system control theoretic concepts". This approach gave a control structure embedded in an adaptive socio-technical system. Such a model shows how different parties contribute to safety regardless of their organizational affiliations.

In the text,

"wear and tear" is the same as loss or damage resulting from ordinary use and exposure.

This text refers to item.

While there is no shortage of studies into the reasons why software projects fail (Ewusi-Mensah, 1997), the major risks of software development (Jones, 1994), or even the factors affecting project success (Cooke-Davies, 2002), the field of software engineering lacks a general model with which to investigate such failures. To date, studies have tended to be surveys of the factors thought to play some part in a failure.

Several researchers have argued that a simple model of accidents is insufficient for dealing with modern technology. A causal-chain model of accidents is useful to investigate the failure of a specific component through wear and tear, or the attribution of the cause can be established through application of a "but for" test. Given the cause, similar accidents can be prevented by checking the same component for wear and tear or other flaws such as structural cracks. However, it is a less useful model when investigating accidents which causes are ultimately not due to physical weaknesses but are due to interactions between components or the failure of the system itself.

Driven by the need to find ways to prevent future accidents, the alternative models reject the simple causal chain model on several grounds. The first is that looking back along the causal chain requires a "stopping rule" to determine when to cease investigating deeper into the system which, it is argued, can be somewhat arbitrary in the choice of cause (Leveson, 2004). The second reason is that such investigative techniques tend to focus attention on the proximate event most closely associated with the accident and direct attention away from the latent, contributory causes.

Where, in the past, it may have been sufficient to seek direct causes of an accident, modern socio-technical systems can produce accidents that are the result of the interaction of different parts of the system rather than a failure of any one part of the system. Turner & Pidgeon (1997) reviewed official investigations into non-natural disasters to arrive at a view that many disasters were man-made and entirely foreseeable. In a major contrast to causal models of accidents, the authors argued that the conditions for the disasters he investigated largely originated from decisions made by upper management.

The view that there was ample evidence of impending disaster available if only someone paid it any attention appears to be shared by investigators other than Turner. However such hindsight bias has been criticised by several researchers, most notably Dekker (2005). Hindsight bias ignores the reality that most operational decisions are made under ambiguous circumstances based on sparse and ambiguous evidence. Instead, Dekker argues, investigators must try hard to understand the circumstances of the time and put aside knowledge of the outcome.

To reason more fully about the interaction of different parts of a socio-technical system, several researchers have proposed a system theoretic model in which the system is expressed as a hierarchy of control levels. Each level of the hierarchy is considered to act on the level below it through the imposition of constraints and directions to achieve emergent properties and to receive feedback. A more useful model for considering total risk was a "top-down, systems oriented approach based on system control theoretic concepts". This approach gave a control structure embedded in an adaptive socio-technical system. Such a model shows how different parties contribute to safety regardless of their organizational affiliations.

In the text,

"which", in "lacks a general model with which to investigate such failures", refers to "investigate".

This text refers to item.

While there is no shortage of studies into the reasons why software projects fail (Ewusi-Mensah, 1997), the major risks of software development (Jones, 1994), or even the factors affecting project success (Cooke-Davies, 2002), the field of software engineering lacks a general model with which to investigate such failures. To date, studies have tended to be surveys of the factors thought to play some part in a failure.

Several researchers have argued that a simple model of accidents is insufficient for dealing with modern technology. A causal-chain model of accidents is useful to investigate the failure of a specific component through wear and tear, or the attribution of the cause can be established through application of a "but for" test. Given the cause, similar accidents can be prevented by checking the same component for wear and tear or other flaws such as structural cracks. However, it is a less useful model when investigating accidents which causes are ultimately not due to physical weaknesses but are due to interactions between components or the failure of the system itself.

Driven by the need to find ways to prevent future accidents, the alternative models reject the simple causal chain model on several grounds. The first is that looking back along the causal chain requires a "stopping rule" to determine when to cease investigating deeper into the system which, it is argued, can be somewhat arbitrary in the choice of cause (Leveson, 2004). The second reason is that such investigative techniques tend to focus attention on the proximate event most closely associated with the accident and direct attention away from the latent, contributory causes.

Where, in the past, it may have been sufficient to seek direct causes of an accident, modern socio-technical systems can produce accidents that are the result of the interaction of different parts of the system rather than a failure of any one part of the system. Turner & Pidgeon (1997) reviewed official investigations into non-natural disasters to arrive at a view that many disasters were man-made and entirely foreseeable. In a major contrast to causal models of accidents, the authors argued that the conditions for the disasters he investigated largely originated from decisions made by upper management.

The view that there was ample evidence of impending disaster available if only someone paid it any attention appears to be shared by investigators other than Turner. However such hindsight bias has been criticised by several researchers, most notably Dekker (2005). Hindsight bias ignores the reality that most operational decisions are made under ambiguous circumstances based on sparse and ambiguous evidence. Instead, Dekker argues, investigators must try hard to understand the circumstances of the time and put aside knowledge of the outcome.

To reason more fully about the interaction of different parts of a socio-technical system, several researchers have proposed a system theoretic model in which the system is expressed as a hierarchy of control levels. Each level of the hierarchy is considered to act on the level below it through the imposition of constraints and directions to achieve emergent properties and to receive feedback. A more useful model for considering total risk was a "top-down, systems oriented approach based on system control theoretic concepts". This approach gave a control structure embedded in an adaptive socio-technical system. Such a model shows how different parties contribute to safety regardless of their organizational affiliations.

In the text,

"shortage" can be correctly replaced by abundance.