### Table 1 Comparison of the on-line string matching algorithms Algorithm Dynamic programming Backward filtering

2007

"... In PAGE 2: ... Current existing on-line string matching algo- rithms for packet inspection can be classified into four categories: dynamic programming, bit parallel, filtering, and automaton algorithms. As summa- rized in Table1 , dynamic programming [3] and bit parallel [4] algorithms are inappropriate for vari- able-length and multiple patterns, and the filtering algorithms [5] have poor worst-case time complexity O(nm), where n and m are the length of the text and patterns, respectively. Only the automaton based algorithms such as Aho-Corasick (AC) [6] support variable-length and multiple patterns, and also have the deterministic worst-case time complexity O(n).... ..."

### Table 1. Lower bounds on the competitive ra- tio of on-line algorithms, depending on the platform type and on the objective function.

2006

"... In PAGE 3: ....1 while in another one (task T on P3) it will be, say, 1.2. Clearly, the minimum of the performance ratios over all ex- ecution scenarios is the desired bound on the competitive ratio of the algorithm: no algorithm can do better than this bound! Because we have three platform types (communication- homogeneous, computation-homogeneous, fully heteroge- neous) and three objective functions (makespan, max-flow, sum-flow), we have nine bounds to establish. Table1 sum- marizes the results, and shows the influence on the platform type on the difficulty of the problem. As expected, mixing both sources of heterogeneity (i.... In PAGE 13: ...ing algorithms. The major contribution of this paper lies on the theoretical side, and is well summarized by Table1 . We have provided a comprehensive set of lower bounds for the competitive ratio of any deterministic scheduling algorithm, for each source of heterogeneity and for each target objec- tive function.... ..."

Cited by 1

### Table 1: Lower bounds on the competitive ratio of on-line algorithms, depending on the platform type and on the objective function.

2005

"... In PAGE 5: ...erformance ratio will be, say, 1.1 while in another one (task T on P3) it will be, say, 1.2. Clearly, the minimum of the performance ratios over all execution scenarios is the desired bound on the competitive ratio of the algorithm: no algorithm can do better than this bound! Because we have three platform types (communication-homogeneous, computation-homoge- neous, fully heterogeneous) and three objective functions (makespan, max-flow, sum-flow), we have nine bounds to establish. Table1 summarizes the results, and shows the influence on the platform type on the difficulty of the problem. As expected, mixing both sources of heterogeneity (i.... In PAGE 20: ... We enforce the one-port model, and we study the impact of heterogeneity on the performance of scheduling algorithms. The major contribution of this paper lies on the theoretical side, and is well summarized by Table1 . We have provided a comprehensive set of lower bounds for the competitive ratio of any deterministic scheduling algorithm, for each source of heterogeneity and for each target objective function.... ..."

### Table 1: Lower bounds on the competitive ratio of on-line algorithms, depending on the platform type and on the objective function.

in The`me NUM

"... In PAGE 6: ....1 while in another one (task T on P3) it will be, say, 1.2. Clearly, the minimum of the performance ratios over all execution scenarios is the desired bound on the competitive ratio of the algorithm: no algorithm can do better than this bound! Because we have three platform types (communication-homogeneous, computation-ho- mogeneous, fully heterogeneous) and three objective functions (makespan, max-flow, sum- flow), we have nine bounds to establish. Table1 summarizes the results, and shows the influence on the platform type on the difficulty of the problem. As expected, mixing both sources of heterogeneity (i.... In PAGE 26: ... We enforce the one-port model, and we study the impact of heterogeneity on the performance of scheduling algorithms. The major contribution of this paper lies on the theoretical side, and is well summarized by Table1 . We have provided a comprehensive set of lower bounds for the competitive ratio of any deterministic scheduling algorithm, for each source of heterogeneity and for each target objective function.... ..."

### Table 1. The On-Line GPTD Algorithm

2003

Cited by 20

### Table 1: Overview of lower bounds (LB) and upper bounds (UB) on the com- petitive ratio of deterministic algorithms for on-line dial-a-ride problems

in information

"... In PAGE 3: ...reemptive version. These results are presented in Section 4. We notice that there is no difference between the preemptive version and the non-preemptive version of the problem if the server has infinite capacity, whence we inherit the matching lower and upper bound of 3 of the preemptive version for this case. An overview of the results is given in Table1 . For the exposition we have omitted to refer to [5] for the first 2-competitive algorithm... ..."

### Table 1: The State of the Art in On-Line Statistical Compressors.

"... In PAGE 9: ... If anything, our Markov approximation of Context should achieve better performance than the original5, since the only extra states that will ever be used will be states that tend to improve the performance of the model. The published performance of on-line stochastic algorithms from the data com- pression literature that have been implemented are shown in Table1 , along with the performance of two popular Unix compression utilities. The utilities are `compress, apos; which is based upon Welch apos;s popular implementation [Wel84] of the Ziv and Lempel apos;s second major string-matching algorithm [ZL78], and `gzip, apos; which is based upon Ziv and Lempel apos;s rst major string-matching construction [ZL77].... ..."

### Table 7. Results for on-line framework tracking an applied goal.

"... In PAGE 94: ...2. Table7 shows the performance of our on-line adaptation framework, together with the onLineAdaptationTr and onLineAdaptationPower functions, for various goals applied to several DSP benchmarks. The starting schedule that is refined is found, as with the other experiments in this section, by using the standard critical path scheduling algorithm.... In PAGE 95: ...053), (T, 215), (P, 0.050), (T, 210), P} In Table7 , the column titled Goal represents the goal that is applied to the application. Also, for a non-negative integer , column denotes the value of a metric of the best schedule found by the on-line adaptation framework, after schedules have been assessed by executing them for some time.... In PAGE 95: ... Also, for a non-negative integer , column denotes the value of a metric of the best schedule found by the on-line adaptation framework, after schedules have been assessed by executing them for some time. For the same exper- iments, which are reported in Table7 , Table 8 shows the times at which different constraints associated with the applied goals, are satisfied. For a given goal that is applied on an application, for non-negative integers and , denotes the number of schedules that have been executed in order to assess them before the th con- straint in the applied goal is satisfied.... ..."

Cited by 1

### Table 1. Comparison to Gaussian RBF with on-line learning.

"... In PAGE 4: ... The selective forgetting algorithm was applied to compute the second layer weights. Table1 summarises the one step ahead prediction results. It can be seen that the proposed neuro-fuzzy learning algorithm has a similar performance to the RBF with 20 hidden units.... ..."